meta:
  version: 5
  updated: 2026-09-05
  owner: Antonio Galanti
  title: Healthcare AI Systems Study Guide
  goal:
    role: Healthcare AI Systems / Forward-Deployed AI Engineer
    emphasis:
    - ML understanding and evaluation
    - AI systems and inference
    - software and data architecture
    - clinical workflow integration
    - production deployment and feedback
  principles:
  - Learn mathematics deeply, but do not let it block applied AI work.
  - Prefer ML-oriented depth over unnecessary mathematical breadth.
  - Learn, build, encounter ignorance, study, then rebuild.
  - Books are resources, not learning objectives.
  - Use a primary source first, a topic-specific fallback when it does not click, and depth material deliberately.
  - Do not schedule duplicate introductory curricula already completed.
  status_values:
  - not-started
  - learning
  - partially-covered
  - comfortable
  - revisit
schedule:
  horizon_months: 18
  stages:
  - stage: 1
    title: Foundations & architecture
    start_month: 1
    end_month: 5
  - stage: 2
    title: Models & applications
    start_month: 4
    end_month: 9
  - stage: 3
    title: Production systems
    start_month: 8
    end_month: 14
  - stage: 4
    title: Clinical integration
    start_month: 13
    end_month: 18
  basis: Proposed study windows, adjustable to pace; not estimates of required hours. Start later topics when their
    relevant prerequisites are ready, not when an entire area is complete.
areas:
- id: mathematics
  title: Mathematics
  code: MATH
  priority: high
  status: revisit
  stage: 1
  months:
    start: 1
    end: 10
  intent: Build mathematical fluency for ML without turning the roadmap into a pure-mathematics degree.
  depends_on: []
  units:
  - id: precalculus-foundations
    title: Precalculus foundations
    sequence: 1
    effort: small
    status: revisit
    objectives:
    - Repair algebraic manipulation, functions, exponents, logarithms, and trigonometry only where they block calculus
      or probability.
    primary:
      resource: professor-leonard-calculus
      content: Selected Precalculus review lessons — functions, graphs, exponentials, logarithms, and trigonometry
      link_status: specific-source
      links:
      - title: Calculus I playlist — start with review lectures 0.1–0.4
        url: https://www.youtube.com/playlist?list=PLF797E961509B4EB5
        verified_on: '2026-09-05'
    exit_criteria:
    - Manipulate functions, logarithms, exponentials, and basic trigonometric expressions without them obstructing
      calculus.
    - Read and sketch the transformations of common functions.
    months:
      start: 1
      end: 1
    topics:
    - Algebraic manipulation
    - Functions and graphs
    - Exponents and logarithms
    - Trigonometry
  - id: calculus-i
    title: Calculus I
    sequence: 2
    effort: large
    status: revisit
    objectives:
    - Limits and continuity; derivatives and the chain rule; antiderivatives; definite integrals as accumulation;
      the Fundamental Theorem of Calculus; basic substitution.
    primary:
      resource: professor-leonard-calculus
      content: 'Calculus I: limits, continuity, derivatives, chain rule, antiderivatives, definite integrals, Fundamental
        Theorem of Calculus, and substitution.'
      link_status: specific-source
      links:
      - title: Calculus I full lectures — including integration
        url: https://www.youtube.com/playlist?list=PLF797E961509B4EB5
        verified_on: '2026-09-05'
    exit_criteria:
    - Explain the chain rule intuitively and apply it through a small computation graph.
    - Interpret a derivative as local sensitivity rather than only a symbolic rule.
    - Explain the Fundamental Theorem of Calculus and the relationship between differentiation and integration.
    - Evaluate basic definite and indefinite integrals, including substitution, and interpret accumulated change.
    months:
      start: 1
      end: 2
    topics:
    - Limits and continuity
    - Derivatives
    - Chain rule
    - Antiderivatives
    - Definite integrals
    - Fundamental Theorem of Calculus
    - Substitution
  - id: calculus-ii
    title: Calculus II — selective integration
    sequence: 4
    effort: medium
    status: revisit
    objectives:
    - Learn integration, substitution, improper integrals, and selected sequences or series needed for probability
      and optimisation.
    primary:
      resource: professor-leonard-calculus
      content: Selected Calculus II lessons — integration techniques, improper integrals, and only the sequences
        and series needed later
      link_status: specific-source
      links:
      - title: Calculus II full lectures — selected integration and series lessons
        url: https://www.youtube.com/playlist?list=PLDesaqWTN6EQ2J4vgsN1HyBeRADEh4Cw-
        verified_on: '2026-09-05'
    exit_criteria:
    - Interpret an integral as accumulation and use it with continuous probability densities.
    - Recognise when substitution or an improper integral is required.
    months:
      start: 4
      end: 5
    topics:
    - Integration techniques
    - Improper integrals
    - Sequences
    - Series
  - id: multivariable-calculus
    title: Multivariable Calculus
    sequence: 6
    effort: large
    status: not-started
    objectives:
    - Work confidently with partial derivatives, gradients, directional derivatives, Jacobians, Hessians, Taylor
      approximations, and multiple integrals.
    primary:
      resource: professor-leonard-calculus
      content: Selected Calculus III lessons — vectors, partial derivatives, gradients, directional derivatives,
        multiple integrals, and constrained optimisation
      link_status: specific-source
      links:
      - title: Calculus III full lectures — selected multivariable lessons
        url: https://www.youtube.com/playlist?list=PLDesaqWTN6ESk16YRmzuJ8f6-rnuy0Ry7
        verified_on: '2026-09-05'
    fallback:
      resource: mathematics-for-ml
      content: Chapter 5 — Vector Calculus
      reason: A condensed ML-oriented route through the same concepts.
      link_status: specific-source
      links:
      - title: 5 Vector Calculus
        url: https://mml-book.github.io/book/mml-book.pdf#page=145
        verified_on: '2026-09-05'
    exit_criteria:
    - Compute and interpret a gradient, Jacobian, and Hessian.
    - Explain what curvature changes about optimisation.
    - Connect multivariable chain rules to backpropagation.
    months:
      start: 8
      end: 10
    topics:
    - Partial derivatives
    - Gradients
    - Directional derivatives
    - Jacobians
    - Hessians
    - Taylor approximations
    - Multiple integrals
    - Constrained optimization
  - id: linear-algebra
    title: Linear Algebra
    sequence: 3
    effort: large
    status: revisit
    objectives:
    - Understand vectors, matrices, transformations, rank, orthogonality, projections, least squares, eigendecomposition,
      and SVD.
    primary:
      resource: mathematics-for-ml
      content: Chapters 2–4 — Linear Algebra, Analytic Geometry, and Matrix Decompositions
      link_status: specific-source
      links:
      - title: 2 Linear Algebra
        url: https://mml-book.github.io/book/mml-book.pdf#page=23
        verified_on: '2026-09-05'
      - title: 3 Analytic Geometry
        url: https://mml-book.github.io/book/mml-book.pdf#page=76
        verified_on: '2026-09-05'
      - title: 4 Matrix Decompositions
        url: https://mml-book.github.io/book/mml-book.pdf#page=104
        verified_on: '2026-09-05'
    fallback:
      resource: mit-1806-linear-algebra
      content: Linear combinations; subspaces; orthogonality and projections; eigenvalues; SVD.
      reason: Strang's geometric explanations are stronger when an algebraic treatment does not click.
      link_status: specific-source
      links:
      - title: 'Lecture 6: Column space and nullspace'
        url: https://ocw.mit.edu/courses/18-06-linear-algebra-spring-2010/resources/lecture-6-column-space-and-nullspace/
        verified_on: '2026-09-05'
      - title: 'Lecture 14: Orthogonal vectors and subspaces'
        url: https://ocw.mit.edu/courses/18-06-linear-algebra-spring-2010/resources/lecture-14-orthogonal-vectors-and-subspaces/
        verified_on: '2026-09-05'
      - title: 'Lecture 15: Projections onto subspaces'
        url: https://ocw.mit.edu/courses/18-06-linear-algebra-spring-2010/resources/lecture-15-projections-onto-subspaces/
        verified_on: '2026-09-05'
      - title: 'Lecture 21: Eigenvalues and eigenvectors'
        url: https://ocw.mit.edu/courses/18-06-linear-algebra-spring-2010/resources/lecture-21-eigenvalues-and-eigenvectors/
        verified_on: '2026-09-05'
      - title: 'Lecture 29: Singular value decomposition'
        url: https://ocw.mit.edu/courses/18-06-linear-algebra-spring-2010/resources/lecture-29-singular-value-decomposition/
        verified_on: '2026-09-05'
    depth:
      resource: aggarwal-linear-algebra
      content: Chapters 2–3 and 7–11, selectively
      reason: Use for an ML-specific second pass, not as the first curriculum.
      link_status: chapter-or-lesson-mapping-unverified
    exit_criteria:
    - Derive least squares and explain it geometrically.
    - Explain PCA as projection onto directions of maximum variance.
    - Track tensor and matrix shapes through a neural network.
    - Explain what SVD decomposes and why it is useful.
    months:
      start: 2
      end: 4
    topics:
    - Vectors and matrices
    - Linear transformations
    - Rank
    - Orthogonality and projections
    - Least squares
    - Eigenvalues and eigenvectors
    - Singular value decomposition
  - id: matrix-calculus
    title: Matrix Calculus
    sequence: 7
    effort: medium
    status: not-started
    objectives:
    - Move from scalar derivatives to vector- and matrix-valued derivatives used in deep learning.
    primary:
      resource: matrix-calculus-dl
      content: Entire article, with the worked neural-network derivation.
      link_status: specific-source
      links:
      - title: Parr & Howard — complete matrix-calculus article
        url: https://explained.ai/matrix-calculus/
        verified_on: '2026-09-05'
    fallback:
      resource: understanding-deep-learning
      content: Appendix B.5 and Chapter 7
      reason: Provides the same machinery inside the model-training context.
      link_status: specific-source
      links:
      - title: 'Appendix: Matrix calculus'
        url: https://github.com/udlbook/udlbook/releases/download/v5.0.2/UnderstandingDeepLearning_05_29_25_C.pdf#page=462
        verified_on: '2026-09-05'
      - title: 'Chapter 7: Gradients and initialization'
        url: https://github.com/udlbook/udlbook/releases/download/v5.0.2/UnderstandingDeepLearning_05_29_25_C.pdf#page=110
        verified_on: '2026-09-05'
    exit_criteria:
    - Derive gradients for affine layers and common losses with correct shapes.
    - Recognize Jacobian-vector and vector-Jacobian products in autodiff.
    months:
      start: 9
      end: 10
    topics:
    - Vector-valued derivatives
    - Matrix-valued derivatives
    - Jacobians
    - Matrix chain rules
    - Neural-network loss gradients
  - id: probability
    title: Probability
    sequence: 5
    effort: large
    status: partially-covered
    objectives:
    - Build durable intuition for conditioning, Bayes, expectation, variance, common distributions, covariance,
      and limit theorems.
    primary:
      resource: stat110
      content: Lectures and problem sets on counting, conditional probability, random variables, expectation, covariance,
        common distributions, and limit theorems.
      link_status: specific-source
      links:
      - title: STAT 110 — lecture videos and topic list
        url: https://stat110.hsites.harvard.edu/youtube
        verified_on: '2026-09-05'
    fallback:
      resource: mathematics-for-ml
      content: Chapter 6 — Probability and Distributions
      reason: Condensed notation-first review tied directly to ML.
      link_status: specific-source
      links:
      - title: 6 Probability and Distributions
        url: https://mml-book.github.io/book/mml-book.pdf#page=178
        verified_on: '2026-09-05'
    exit_criteria:
    - Use conditioning and Bayes without treating them as formula substitution.
    - Derive expectation and variance for simple random variables.
    - Explain covariance, correlation, and conditional independence.
    months:
      start: 5
      end: 8
    topics:
    - Counting
    - Conditional probability
    - Bayes’ theorem
    - Random variables
    - Expectation and variance
    - Covariance
    - Common distributions
    - Limit theorems
  - id: statistics-for-ml
    title: Statistics & statistical learning
    sequence: 5
    effort: large
    status: revisit
    objectives:
    - Understand sampling variability, estimators, confidence intervals, hypothesis tests, effect sizes, multiple
      comparisons, and the limits of statistical significance.
    - Connect statistical inference to clinical evidence and honest model evaluation.
    primary:
      resource: islp
      content: 'Chapters 2–5, 6 and 13: statistical learning, regression and classification, resampling, regularization,
        and multiple testing. Focus on uncertainty, statistical versus predictive inference, validation and model
        selection.'
      link_status: specific-source
      links:
      - title: 2 Statistical Learning (PDF p. 25)
        url: https://drive.google.com/uc?export=download&id=1ajFkHO6zjrdGNqhqW1jKBZdiNGh_8YQ1#page=25
        verified_on: '2026-09-05'
      - title: 3 Linear Regression (PDF p. 78)
        url: https://drive.google.com/uc?export=download&id=1ajFkHO6zjrdGNqhqW1jKBZdiNGh_8YQ1#page=78
        verified_on: '2026-09-05'
      - title: 4 Classification (PDF p. 144)
        url: https://drive.google.com/uc?export=download&id=1ajFkHO6zjrdGNqhqW1jKBZdiNGh_8YQ1#page=144
        verified_on: '2026-09-05'
      - title: 5 Resampling Methods (PDF p. 209)
        url: https://drive.google.com/uc?export=download&id=1ajFkHO6zjrdGNqhqW1jKBZdiNGh_8YQ1#page=209
        verified_on: '2026-09-05'
      - title: 6 Linear Model Selection and Regularization (PDF p. 237)
        url: https://drive.google.com/uc?export=download&id=1ajFkHO6zjrdGNqhqW1jKBZdiNGh_8YQ1#page=237
        verified_on: '2026-09-05'
      - title: 13 Multiple Testing (PDF p. 563)
        url: https://drive.google.com/uc?export=download&id=1ajFkHO6zjrdGNqhqW1jKBZdiNGh_8YQ1#page=563
        verified_on: '2026-09-05'
    exit_criteria:
    - Distinguish an estimand, estimator, estimate, confidence interval, and hypothesis test.
    - Interpret effect size and uncertainty without reducing evidence to a p-value.
    - Explain sampling bias, confounding, multiple testing, and why statistical significance does not imply clinical
      utility.
    note: 'ISLP is the main statistical-ML bridge. This unit and Classical ML share readings: study them once, then
      apply them. Use a foundations supplement only if a specific inference concept needs a second explanation.'
    months:
      start: 5
      end: 8
    topics:
    - Sampling variability
    - Estimators
    - Confidence intervals
    - Hypothesis tests
    - Effect sizes
    - Multiple comparisons
    - Validation and model selection
  notes:
  - Build mathematical fluency for ML without turning the roadmap into a pure-mathematics degree.
  - Calculus I includes integrals. Calculus II extends integration selectively.
  - Start Linear Algebra in month 2; Calculus II is not its prerequisite.
  - Probability and Statistics are separate topics studied alongside one another. ISLP is the statistical-ML bridge.
  - Mathematics runs alongside applied work. Month windows are provisional pacing, not a requirement to finish all
    mathematics before starting ML.
  reference_resources:
  - calculus-lifesaver
- id: classical-ml
  title: Classical Machine Learning
  code: CML
  priority: medium
  status: revisit
  stage: 1
  months:
    start: 2
    end: 5
  intent: Consolidate evaluation and statistical judgment; avoid repeating beginner courses.
  depends_on:
  - mathematics
  units:
  - id: statistical-learning-foundations
    title: Statistical learning foundations
    effort: medium
    status: comfortable
    primary:
      resource: islp
      content: Chapters 1–2 — Introduction and Statistical Learning
      link_status: specific-source
      links:
      - title: 1 Introduction (PDF p. 12)
        url: https://drive.google.com/uc?export=download&id=1ajFkHO6zjrdGNqhqW1jKBZdiNGh_8YQ1#page=12
        verified_on: '2026-09-05'
      - title: 2 Statistical Learning (PDF p. 25)
        url: https://drive.google.com/uc?export=download&id=1ajFkHO6zjrdGNqhqW1jKBZdiNGh_8YQ1#page=25
        verified_on: '2026-09-05'
    fallback:
      resource: geron-pytorch
      content: Chapters 1–2 — ML landscape and end-to-end project
      reason: Use when an idea needs a concrete Scikit-Learn workflow.
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch01.html
        title: 1. The Machine Learning Landscape
        verified_on: '2026-09-05'
      - &id001
        url: https://www.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch02.html
        title: 2. End-to-End Machine Learning Project
        verified_on: '2026-09-05'
    exit_criteria:
    - Distinguish inference from prediction and explain the bias-variance trade-off.
    - Frame a clinical prediction problem with an honest target and baseline.
    months:
      start: 2
      end: 2
    sequence: 1
    topics:
    - Statistical learning
    - Prediction and inference
    - Bias–variance trade-off
    - Supervised and unsupervised learning
  - id: regression-classification
    title: Regression & classification
    effort: large
    status: comfortable
    primary:
      resource: islp
      content: Chapters 3–4 — Linear Regression and Classification
      link_status: specific-source
      links:
      - title: 3 Linear Regression (PDF p. 78)
        url: https://drive.google.com/uc?export=download&id=1ajFkHO6zjrdGNqhqW1jKBZdiNGh_8YQ1#page=78
        verified_on: '2026-09-05'
      - title: 4 Classification (PDF p. 144)
        url: https://drive.google.com/uc?export=download&id=1ajFkHO6zjrdGNqhqW1jKBZdiNGh_8YQ1#page=144
        verified_on: '2026-09-05'
    fallback:
      resource: geron-pytorch
      content: Chapters 3–4 — Classification and Training Models
      reason: Stronger implementation path and operational metric examples.
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch03.html
        title: 3. Classification
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch04.html
        title: 4. Training Models
        verified_on: '2026-09-05'
    exit_criteria:
    - Choose and interpret metrics for imbalanced clinical classification.
    - Explain calibration, discrimination, regularization, and threshold trade-offs.
    months:
      start: 2
      end: 3
    sequence: 2
    topics:
    - Linear regression
    - Logistic regression
    - Linear discriminant analysis
    - K-nearest neighbors
    - Classification metrics
  - id: resampling-selection
    title: Resampling, selection & regularization
    effort: medium
    status: partially-covered
    primary:
      resource: islp
      content: Chapters 5–6 — Resampling Methods and Linear Model Selection and Regularization
      link_status: specific-source
      links:
      - title: 5 Resampling Methods (PDF p. 209)
        url: https://drive.google.com/uc?export=download&id=1ajFkHO6zjrdGNqhqW1jKBZdiNGh_8YQ1#page=209
        verified_on: '2026-09-05'
      - title: 6 Linear Model Selection and Regularization (PDF p. 237)
        url: https://drive.google.com/uc?export=download&id=1ajFkHO6zjrdGNqhqW1jKBZdiNGh_8YQ1#page=237
        verified_on: '2026-09-05'
    fallback:
      resource: geron-pytorch
      content: Chapter 2 model selection workflow and Chapter 4 regularized models
      reason: More direct implementation and pipeline context.
      link_status: publisher-chapter-links
      links:
      - *id001
    exit_criteria:
    - Select a validation design that prevents patient or temporal leakage.
    - Explain when cross-validation estimates are misleading.
    months:
      start: 3
      end: 4
    sequence: 3
    topics:
    - Cross-validation
    - Bootstrap
    - Feature selection
    - Regularization
    - Ridge regression
    - Lasso
  - id: trees-ensembles
    title: Trees & ensembles
    effort: medium
    status: comfortable
    primary:
      resource: geron-pytorch
      content: Chapters 5–6 — Decision Trees and Ensemble Learning
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch05.html
        title: 5. Decision Trees
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch06.html
        title: 6. Ensemble Learning and Random Forests
        verified_on: '2026-09-05'
    fallback:
      resource: islp
      content: Chapter 8 — Tree-Based Methods
      reason: More statistical framing and comparison of methods.
      link_status: specific-source
      links:
      - title: 8 Tree-Based Methods (PDF p. 338)
        url: https://drive.google.com/uc?export=download&id=1ajFkHO6zjrdGNqhqW1jKBZdiNGh_8YQ1#page=338
        verified_on: '2026-09-05'
    exit_criteria:
    - Build defensible tabular baselines before a multimodal model.
    - Explain variance reduction, boosting, and feature-importance caveats.
    months:
      start: 4
      end: 4
    sequence: 4
    topics:
    - Decision trees
    - Bagging
    - Random forests
    - Boosting
    - Ensembles
  - id: unsupervised-dimensionality
    title: Unsupervised learning & dimensionality reduction
    effort: medium
    status: partially-covered
    primary:
      resource: geron-pytorch
      content: Chapters 7–8 — Dimensionality Reduction and Unsupervised Learning
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch07.html
        title: 7. Dimensionality Reduction
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch08.html
        title: 8. Unsupervised Learning Techniques
        verified_on: '2026-09-05'
    fallback:
      resource: islp
      content: Chapter 12 — Unsupervised Learning
      reason: Compact statistical account of PCA and clustering.
      link_status: specific-source
      links:
      - title: 12 Unsupervised Learning (PDF p. 509)
        url: https://drive.google.com/uc?export=download&id=1ajFkHO6zjrdGNqhqW1jKBZdiNGh_8YQ1#page=509
        verified_on: '2026-09-05'
    exit_criteria:
    - Explain PCA, clustering, and anomaly detection without overclaiming clinical meaning.
    months:
      start: 4
      end: 5
    sequence: 5
    topics:
    - Principal component analysis
    - Clustering
    - Dimensionality reduction
    - Unsupervised evaluation
  notes:
  - Consolidate evaluation and statistical judgment; avoid repeating beginner courses.
  - Use ISLP as the main statistical-ML bridge and Géron for implementation. Reuse readings from Statistics; avoid
    repeating completed beginner courses.
- id: deep-learning
  title: Deep Learning
  code: DL
  priority: high
  status: revisit
  stage: 2
  months:
    start: 5
    end: 8
  intent: Replace course completion with mathematical and diagnostic depth.
  depends_on:
  - mathematics
  - classical-ml
  units:
  - id: neural-network-mechanics
    title: Network mechanics & losses
    effort: large
    status: partially-covered
    primary:
      resource: understanding-deep-learning
      content: Chapters 2–5 — Supervised learning through loss functions
      link_status: specific-source
      links:
      - title: 'Chapter 2: Supervised learning'
        url: https://github.com/udlbook/udlbook/releases/download/v5.0.2/UnderstandingDeepLearning_05_29_25_C.pdf#page=31
        verified_on: '2026-09-05'
      - title: 'Chapter 3: Shallow neural networks'
        url: https://github.com/udlbook/udlbook/releases/download/v5.0.2/UnderstandingDeepLearning_05_29_25_C.pdf#page=39
        verified_on: '2026-09-05'
      - title: 'Chapter 4: Deep neural networks'
        url: https://github.com/udlbook/udlbook/releases/download/v5.0.2/UnderstandingDeepLearning_05_29_25_C.pdf#page=55
        verified_on: '2026-09-05'
      - title: 'Chapter 5: Loss functions'
        url: https://github.com/udlbook/udlbook/releases/download/v5.0.2/UnderstandingDeepLearning_05_29_25_C.pdf#page=70
        verified_on: '2026-09-05'
    fallback:
      resource: nn-zero-to-hero
      content: Lectures 1–3 — micrograd, language modelling, and MLPs
      reason: Code-first intuition when the notation feels detached from implementation.
      link_status: specific-source
      links:
      - title: Building micrograd
        url: https://www.youtube.com/watch?v=VMj-3S1tku0
        verified_on: '2026-09-05'
      - title: 'Language modelling: makemore'
        url: https://www.youtube.com/watch?v=PaCmpygFfXo
        verified_on: '2026-09-05'
      - title: MLP
        url: https://www.youtube.com/watch?v=TCH_1BHY58I
        verified_on: '2026-09-05'
    exit_criteria:
    - Derive the forward pass and loss for a small network.
    - Explain capacity, activation functions, and cross-entropy precisely.
    months:
      start: 5
      end: 5
    sequence: 1
    topics:
    - Supervised learning
    - Network layers
    - Activation functions
    - Loss functions
    - Forward propagation
  - id: optimization-backprop
    title: Optimization, backprop & initialization
    effort: large
    status: partially-covered
    primary:
      resource: understanding-deep-learning
      content: Chapters 6–7 — Fitting Models; Gradients and Initialization
      link_status: specific-source
      links:
      - title: 'Chapter 6: Fitting models'
        url: https://github.com/udlbook/udlbook/releases/download/v5.0.2/UnderstandingDeepLearning_05_29_25_C.pdf#page=91
        verified_on: '2026-09-05'
      - title: 'Chapter 7: Gradients and initialization'
        url: https://github.com/udlbook/udlbook/releases/download/v5.0.2/UnderstandingDeepLearning_05_29_25_C.pdf#page=110
        verified_on: '2026-09-05'
    fallback:
      resource: nn-zero-to-hero
      content: Lectures 4–5 — activations, gradients, BatchNorm, and manual backprop
      reason: Exceptional debugging-level intuition for gradient flow.
      link_status: specific-source
      links:
      - title: Activations, gradients and BatchNorm
        url: https://www.youtube.com/watch?v=P6sfmUTpUmc
        verified_on: '2026-09-05'
      - title: Manual backpropagation
        url: https://www.youtube.com/watch?v=q8SA3rM6ckI
        verified_on: '2026-09-05'
    exit_criteria:
    - Implement backprop for a small computation graph.
    - Diagnose vanishing, exploding, or badly scaled activations and gradients.
    months:
      start: 5
      end: 6
    sequence: 2
    topics:
    - Gradient descent
    - Backpropagation
    - Initialization
    - Activation and gradient diagnostics
    - Batch normalization
  - id: generalization-regularization
    title: Generalization & regularization
    effort: medium
    status: partially-covered
    primary:
      resource: understanding-deep-learning
      content: Chapters 8–9 and 20 — Performance, Regularization, and Why Deep Learning Works
      link_status: specific-source
      links:
      - title: 'Chapter 8: Measuring performance'
        url: https://github.com/udlbook/udlbook/releases/download/v5.0.2/UnderstandingDeepLearning_05_29_25_C.pdf#page=132
        verified_on: '2026-09-05'
      - title: 'Chapter 9: Regularization'
        url: https://github.com/udlbook/udlbook/releases/download/v5.0.2/UnderstandingDeepLearning_05_29_25_C.pdf#page=152
        verified_on: '2026-09-05'
      - title: 'Chapter 20: Why does deep learning work?'
        url: https://github.com/udlbook/udlbook/releases/download/v5.0.2/UnderstandingDeepLearning_05_29_25_C.pdf#page=416
        verified_on: '2026-09-05'
    fallback:
      resource: geron-pytorch
      content: Chapter 11 — Training Deep Neural Networks
      reason: Practical regularization, normalization, optimizer, and scheduling recipes.
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch11.html
        title: 11. Training Deep Neural Networks
        verified_on: '2026-09-05'
    exit_criteria:
    - Separate optimization failure from generalization failure.
    - Design ablations that test rather than decorate a model claim.
    months:
      start: 6
      end: 7
    sequence: 3
    topics:
    - Generalization
    - Regularization
    - Capacity
    - Overfitting
    - Deep-learning theory
  - id: cnn-clinical-imaging
    title: CNNs & clinical imaging
    effort: large
    status: learning
    primary:
      resource: understanding-deep-learning
      content: Chapters 10–11 — Convolutional and Residual Networks
      link_status: specific-source
      links:
      - title: 'Chapter 10: Convolutional networks'
        url: https://github.com/udlbook/udlbook/releases/download/v5.0.2/UnderstandingDeepLearning_05_29_25_C.pdf#page=175
        verified_on: '2026-09-05'
      - title: 'Chapter 11: Residual networks'
        url: https://github.com/udlbook/udlbook/releases/download/v5.0.2/UnderstandingDeepLearning_05_29_25_C.pdf#page=200
        verified_on: '2026-09-05'
    fallback:
      resource: geron-pytorch
      content: Chapter 12 — Deep Computer Vision Using CNNs
      reason: PyTorch implementation and transfer-learning path.
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch12.html
        title: 12. Deep Computer Vision Using Convolutional Neural Networks
        verified_on: '2026-09-05'
    exit_criteria:
    - Explain receptive fields, equivariance, residual connections, and transfer learning.
    - Design a leakage-safe CXR baseline and error analysis.
    months:
      start: 7
      end: 8
    sequence: 4
    topics:
    - Convolution
    - Receptive fields
    - CNN architectures
    - Residual networks
    - Clinical-image evaluation
  - id: transformers
    title: Transformers
    effort: large
    status: learning
    primary:
      resource: understanding-deep-learning
      content: Chapter 12 — Transformers
      link_status: specific-source
      links:
      - title: 'Chapter 12: Transformers'
        url: https://github.com/udlbook/udlbook/releases/download/v5.0.2/UnderstandingDeepLearning_05_29_25_C.pdf#page=221
        verified_on: '2026-09-05'
    fallback:
      resource: nn-zero-to-hero
      content: Lecture 7 — Build GPT from scratch
      reason: Converts attention equations into an inspectable implementation.
      link_status: specific-source
      links:
      - title: Build GPT from scratch
        url: https://www.youtube.com/watch?v=kCc8FmEb1nY
        verified_on: '2026-09-05'
    depth:
      resource: geron-pytorch
      content: Chapters 15–17 — NLP transformers, multimodal transformers, and efficient transformers
      reason: Use selectively for implementation breadth and efficiency.
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch15.html
        title: 15. Transformers for Natural Language Processing and Chatbots
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch16.html
        title: 16. Vision and Multimodal Transformers
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch17.html
        title: 17. Speeding Up Transformers
        verified_on: '2026-09-05'
    exit_criteria:
    - Explain self-attention, positional information, masking, and the residual stream.
    - Trace tensor shapes through multi-head attention.
    months:
      start: 7
      end: 8
    sequence: 5
    topics:
    - Self-attention
    - Multi-head attention
    - Positional encoding
    - Transformer blocks
    - Autoregressive models
  notes:
  - Replace course completion with mathematical and diagnostic depth.
  - Prince supplies mathematical and diagnostic depth; Karpathy supplies implementation intuition. Avoid another
    full introductory Deep Learning course.
- id: llm-applications
  title: LLM Application Engineering
  code: LLM
  priority: high
  status: learning
  stage: 2
  months:
    start: 7
    end: 10
  intent: Build useful foundation-model systems with evaluation before orchestration complexity.
  depends_on:
  - deep-learning
  units:
  - id: tokens-embeddings
    title: Tokens & embeddings
    effort: medium
    status: learning
    primary:
      resource: hands-on-llms
      content: Chapters 1–3 — LLMs, Tokens and Embeddings, and Transformer Internals
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/hands-on-large-language/9781098150952/ch01.html
        title: 1. An Introduction to Large Language Models
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/hands-on-large-language/9781098150952/ch02.html
        title: 2. Tokens and Embeddings
        verified_on: '2026-09-05'
      - &id009
        url: https://www.oreilly.com/library/view/hands-on-large-language/9781098150952/ch03.html
        title: 3. Looking Inside Large Language Models
        verified_on: '2026-09-05'
    fallback:
      resource: nn-zero-to-hero
      content: Lecture 8 — Build the GPT tokenizer
      reason: Makes BPE and tokenizer failure modes concrete.
      link_status: specific-source
      links:
      - title: Build the GPT tokenizer
        url: https://www.youtube.com/watch?v=zduSFxRajkE
        verified_on: '2026-09-05'
    exit_criteria:
    - Explain tokenization and embedding trade-offs in retrieval and generation.
    - Connect KV caching to the autoregressive forward pass.
    months:
      start: 7
      end: 7
    sequence: 1
    topics:
    - Tokenization
    - Byte-pair encoding
    - Embeddings
    - Context representation
    - Transformer internals
  - id: prompt-structured-output
    title: Prompting & structured outputs
    effort: small
    status: learning
    primary:
      resource: ai-engineering
      content: Chapters 2 and 5 — Foundation-model behaviour and Prompt Engineering
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/ai-engineering/9781098166298/ch02.html
        title: 2. Understanding Foundation Models
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/ai-engineering/9781098166298/ch05.html
        title: 5. Prompt Engineering
        verified_on: '2026-09-05'
    fallback:
      resource: hands-on-llms
      content: Chapter 6 — Prompt Engineering
      reason: More code-led examples and constrained generation.
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/hands-on-large-language/9781098150952/ch06.html
        title: 6. Prompt Engineering
        verified_on: '2026-09-05'
    exit_criteria:
    - Version prompts and evaluate them against a defined task set.
    - Design a structured-output contract and failure handling.
    months:
      start: 7
      end: 8
    sequence: 2
    topics:
    - Prompt design
    - In-context learning
    - Structured outputs
    - Schema constraints
    - Failure modes
  - id: rag
    title: Retrieval-augmented generation
    effort: large
    status: not-started
    primary:
      resource: ai-engineering
      content: Chapter 6 — RAG and Agents, RAG sections
      link_status: publisher-chapter-links
      links:
      - &id002
        url: https://www.oreilly.com/library/view/ai-engineering/9781098166298/ch06.html
        title: 6. RAG and Agents
        verified_on: '2026-09-05'
    fallback:
      resource: hands-on-llms
      content: Chapter 8 — Semantic Search and RAG
      reason: Stronger worked pipeline from dense retrieval through reranking and evaluation.
      link_status: publisher-chapter-links
      links:
      - &id003
        url: https://www.oreilly.com/library/view/hands-on-large-language/9781098150952/ch08.html
        title: 8. Semantic Search and Retrieval-Augmented Generation
        verified_on: '2026-09-05'
    exit_criteria:
    - Separate retrieval, reranking, generation, and end-to-end evaluation.
    - Diagnose whether a failure came from missing context, retrieval, or generation.
    months:
      start: 8
      end: 9
    sequence: 3
    topics:
    - Retrieval
    - Chunking
    - Embeddings
    - Vector search
    - Reranking
    - Grounded generation
    - RAG evaluation
  - id: agents
    title: Agents & tool use
    effort: medium
    status: not-started
    primary:
      resource: ai-engineering
      content: Chapters 6 and 10 — Agents; Architecture and User Feedback
      link_status: publisher-chapter-links
      links:
      - *id002
      - &id006
        url: https://www.oreilly.com/library/view/ai-engineering/9781098166298/ch10.html
        title: 10. AI Engineering Architecture and User Feedback
        verified_on: '2026-09-05'
    fallback:
      resource: hands-on-llms
      content: Chapter 7 — Advanced Text Generation Techniques and Tools
      reason: A compact code-first bridge to tools, chains, memory, and ReAct.
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/hands-on-large-language/9781098150952/ch07.html
        title: 7. Advanced Text Generation Techniques and Tools
        verified_on: '2026-09-05'
    exit_criteria:
    - Decide when an agent is justified over a deterministic workflow.
    - Evaluate tool selection, arguments, recovery, and task completion separately.
    months:
      start: 9
      end: 10
    sequence: 4
    topics:
    - Tool calling
    - Planning loops
    - State and memory
    - Agent evaluation
    - Human oversight
  - id: foundation-model-evaluation
    title: Foundation-model evaluation
    effort: large
    status: not-started
    primary:
      resource: ai-engineering
      content: Chapters 3–4 — Evaluation Methodology and Evaluate AI Systems
      link_status: publisher-chapter-links
      links:
      - &id017
        url: https://www.oreilly.com/library/view/ai-engineering/9781098166298/ch03.html
        title: 3. Evaluation Methodology
        verified_on: '2026-09-05'
      - &id010
        url: https://www.oreilly.com/library/view/ai-engineering/9781098166298/ch04.html
        title: 4. Evaluate AI Systems
        verified_on: '2026-09-05'
    fallback:
      resource: hands-on-llms
      content: Chapters 8 and 12 evaluation sections
      reason: Worked retrieval and generative evaluation examples.
      link_status: publisher-chapter-links
      links:
      - *id003
      - &id004
        url: https://www.oreilly.com/library/view/hands-on-large-language/9781098150952/ch12.html
        title: 12. Fine-Tuning Generation Models
        verified_on: '2026-09-05'
    exit_criteria:
    - Define task-specific criteria, datasets, judges, and human review.
    - Report quality, latency, and cost together rather than as separate afterthoughts.
    months:
      start: 7
      end: 10
    sequence: 5
    topics:
    - Evaluation criteria
    - Datasets and rubrics
    - Model-based evaluation
    - Human evaluation
    - Error analysis
  - id: finetuning-dataset-engineering
    title: Finetuning & dataset engineering
    effort: large
    status: not-started
    primary:
      resource: ai-engineering
      content: Chapters 7–8 — Finetuning and Dataset Engineering
      link_status: publisher-chapter-links
      links:
      - &id007
        url: https://www.oreilly.com/library/view/ai-engineering/9781098166298/ch07.html
        title: 7. Finetuning
        verified_on: '2026-09-05'
      - &id005
        url: https://www.oreilly.com/library/view/ai-engineering/9781098166298/ch08.html
        title: 8. Dataset Engineering
        verified_on: '2026-09-05'
    fallback:
      resource: hands-on-llms
      content: Chapters 10–12 — Embedding, representation, and generation model finetuning
      reason: Detailed implementation path for contrastive, PEFT, QLoRA, and preference tuning.
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/hands-on-large-language/9781098150952/ch10.html
        title: 10. Creating Text Embedding Models
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/hands-on-large-language/9781098150952/ch11.html
        title: 11. Fine-Tuning Representation Models for Classification
        verified_on: '2026-09-05'
      - *id004
    exit_criteria:
    - Choose among prompting, RAG, and finetuning based on evidence.
    - Build and audit a task dataset without contaminating evaluation.
    months:
      start: 9
      end: 10
    sequence: 6
    topics:
    - Supervised fine-tuning
    - Parameter-efficient fine-tuning
    - Dataset curation
    - Data quality
    - Synthetic data
  notes:
  - Build useful foundation-model systems with evaluation before orchestration complexity.
  - Use Hands-On Large Language Models for practical LLM understanding and AI Engineering for evaluation, RAG, agents,
    latency, cost and application design.
- id: ml-systems
  title: ML Systems
  code: MLSYS
  priority: very-high
  status: learning
  stage: 3
  months:
    start: 9
    end: 13
  intent: Focus on the parts of production ML that differ from ordinary DevOps.
  depends_on:
  - classical-ml
  - deep-learning
  - software-architecture
  units:
  - id: system-framing
    title: System framing & requirements
    effort: medium
    status: learning
    primary:
      resource: designing-ml-systems
      content: Chapters 1–2 — Overview and ML Systems Design
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/ch01.html
        title: 1. Overview of Machine Learning Systems
        verified_on: '2026-09-05'
      - &id016
        url: https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/ch02.html
        title: 2. Introduction to Machine Learning Systems Design
        verified_on: '2026-09-05'
    fallback:
      resource: ai-engineering
      content: Chapter 1 — Planning AI Applications
      reason: Better fit when the system is built around foundation models.
      link_status: publisher-chapter-links
      links:
      - &id019
        url: https://www.oreilly.com/library/view/ai-engineering/9781098166298/ch01.html
        title: 1. Introduction to Building AI Applications with Foundation Models
        verified_on: '2026-09-05'
    exit_criteria:
    - Translate clinical and business goals into model and system requirements.
    - State reliability, latency, maintainability, and adaptation requirements explicitly.
    months:
      start: 9
      end: 9
    sequence: 1
    topics:
    - ML objectives
    - System requirements
    - Baselines
    - Constraints
    - Failure modes
  - id: data-lifecycle
    title: Training data & feature lifecycle
    effort: large
    status: learning
    primary:
      resource: designing-ml-systems
      content: Chapters 3–5 — Data Engineering, Training Data, and Feature Engineering
      link_status: publisher-chapter-links
      links:
      - &id015
        url: https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/ch03.html
        title: 3. Data Engineering Fundamentals
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/ch04.html
        title: 4. Training Data
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/ch05.html
        title: 5. Feature Engineering
        verified_on: '2026-09-05'
    fallback:
      resource: ai-engineering
      content: Chapter 8 — Dataset Engineering
      reason: Better treatment when data is for finetuning or foundation-model evaluation.
      link_status: publisher-chapter-links
      links:
      - *id005
    exit_criteria:
    - Version data lineage, manifests, splits, transformations, and labels reproducibly.
    - Identify leakage, feedback loops, and distribution mismatch before deployment.
    months:
      start: 9
      end: 10
    sequence: 2
    topics:
    - Data collection
    - Labeling
    - Training data
    - Feature engineering
    - Data lineage
    - Data quality
  - id: deployment-serving
    title: Deployment & serving
    effort: medium
    status: partially-covered
    primary:
      resource: designing-ml-systems
      content: Chapter 7 — Model Deployment and Prediction Service
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/ch07.html
        title: 7. Model Deployment and Prediction Service
        verified_on: '2026-09-05'
    fallback:
      resource: ai-engineering
      content: Chapters 9–10 — Inference Optimization and AI Engineering Architecture
      reason: Extends the serving story to foundation-model systems.
      link_status: publisher-chapter-links
      links:
      - &id008
        url: https://www.oreilly.com/library/view/ai-engineering/9781098166298/ch09.html
        title: 9. Inference Optimization
        verified_on: '2026-09-05'
      - *id006
    exit_criteria:
    - Choose batch, synchronous online, asynchronous, or streaming prediction deliberately.
    - Design versioning, rollback, shadowing, and compatibility boundaries.
    months:
      start: 10
      end: 11
    sequence: 3
    topics:
    - Batch prediction
    - Online prediction
    - Model serving
    - Rollouts
    - Service boundaries
  - id: monitoring-shift
    title: Monitoring & distribution shift
    effort: large
    status: not-started
    primary:
      resource: designing-ml-systems
      content: Chapter 8 — Data Distribution Shifts and Monitoring
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/ch08.html
        title: 8. Data Distribution Shifts and Monitoring
        verified_on: '2026-09-05'
    fallback:
      resource: ai-engineering
      content: Chapter 10 — Monitoring, Observability, and User Feedback
      reason: Adds application-level and conversational feedback signals.
      link_status: publisher-chapter-links
      links:
      - *id006
    exit_criteria:
    - Separate service health, data quality, model quality, and clinical outcome monitoring.
    - Define actionable alerts and retraining triggers rather than a metrics museum.
    months:
      start: 11
      end: 12
    sequence: 4
    topics:
    - Data drift
    - Concept drift
    - Performance monitoring
    - Feedback loops
    - Alert design
  - id: continual-learning-testing
    title: Continual learning & production testing
    effort: medium
    status: not-started
    primary:
      resource: designing-ml-systems
      content: Chapter 9 — Continual Learning and Test in Production
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/ch09.html
        title: 9. Continual Learning and Test in Production
        verified_on: '2026-09-05'
    fallback:
      resource: geron-pytorch
      content: Chapter 2 — Launch, Monitor, and Maintain Your System
      reason: A concrete end-to-end workflow anchor.
      link_status: publisher-chapter-links
      links:
      - *id001
    exit_criteria:
    - Choose shadow, canary, A/B, or interleaving tests with patient-safety constraints.
    - Explain stateless retraining versus stateful continual learning.
    months:
      start: 12
      end: 13
    sequence: 5
    topics:
    - Continual learning
    - Shadow deployment
    - A/B testing
    - Canary testing
    - Production validation
  notes:
  - Focus on the parts of production ML that differ from ordinary DevOps.
- id: inference-systems
  title: AI Inference Systems
  code: INFER
  priority: very-high
  status: not-started
  stage: 3
  months:
    start: 10
    end: 14
  intent: Close the largest gap between model knowledge and deployable local or private AI.
  depends_on:
  - deep-learning
  - ml-systems
  units:
  - id: precision-memory
    title: Precision, memory & quantization
    effort: large
    status: not-started
    primary:
      resource: ai-engineering
      content: Chapters 7 and 9 — numerical representations, quantization, and inference optimization
      link_status: publisher-chapter-links
      links:
      - *id007
      - *id008
    fallback:
      resource: geron-pytorch
      content: Appendix B — Mixed Precision and Quantization
      reason: Concrete PyTorch treatment from training through LLM quantization.
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/hands-on-machine-learning/9798341607972/app02.html
        title: B. Mixed Precision and Quantization
        verified_on: '2026-09-05'
    exit_criteria:
    - Estimate weights, activations, optimizer state, and KV-cache memory.
    - Explain FP32, FP16, BF16, INT8, and INT4 trade-offs.
    months:
      start: 10
      end: 11
    sequence: 1
    topics:
    - Numeric precision
    - Model memory
    - Quantization
    - Weight compression
    - Accuracy trade-offs
  - id: prefill-decode-kv
    title: Prefill, decode & KV cache
    effort: medium
    status: not-started
    primary:
      resource: vllm-docs
      content: Read Chunked Prefill for the two execution phases, then Automatic Prefix Caching for reusable KV
        state. These are separate source pages for this combined topic.
      links:
      - title: 'Chunked prefill: prefill/decode bottlenecks'
        url: https://docs.vllm.ai/en/latest/configuration/optimization/#chunked-prefill
        verified_on: '2026-09-05'
      - title: Automatic Prefix Caching
        url: https://docs.vllm.ai/en/latest/features/automatic_prefix_caching/
        verified_on: '2026-09-05'
      link_status: verified-specific-pages
    fallback:
      resource: hands-on-llms
      content: Chapter 3 — generation and caching keys and values
      reason: Visual model-level explanation before engine internals.
      link_status: publisher-chapter-links
      links:
      - *id009
    exit_criteria:
    - Explain why prefill and decode have different bottlenecks.
    - Relate context length, concurrency, KV-cache growth, and latency.
    months:
      start: 11
      end: 12
    sequence: 2
    topics:
    - Prefill phase
    - Decode phase
    - KV cache
    - Paged memory
    - Prefix caching
  - id: batching-throughput
    title: Batching, throughput & latency
    effort: medium
    status: not-started
    primary:
      resource: vllm-docs
      content: Continuous batching, scheduling, benchmarking, and serving configuration
      links:
      - title: Chunked prefill and performance tuning
        url: https://docs.vllm.ai/en/latest/configuration/optimization/#chunked-prefill
        verified_on: '2026-09-05'
      - title: 'Production Metrics: latency, queues and tokens'
        url: https://docs.vllm.ai/en/latest/usage/metrics/
        verified_on: '2026-09-05'
      link_status: verified-specific-pages
    fallback:
      resource: vllm-docs
      content: 'Production Metrics: connect scheduling decisions to latency and throughput measurements.'
      reason: Use concrete engine measurements when the scheduling description is too abstract.
      links:
      - title: Production Metrics
        url: https://docs.vllm.ai/en/latest/usage/metrics/
        verified_on: '2026-09-05'
      link_status: verified-specific-pages
    exit_criteria:
    - Explain continuous batching and head-of-line blocking.
    - Choose metrics for time-to-first-token, inter-token latency, throughput, and tail latency.
    months:
      start: 12
      end: 13
    sequence: 3
    topics:
    - Continuous batching
    - Request scheduling
    - Throughput
    - Latency
    - Benchmarking
  - id: parallelism-scheduling
    title: Parallelism & GPU scheduling
    effort: large
    status: not-started
    primary:
      resource: tensorrt-llm-docs
      content: Tensor parallelism, pipeline parallelism, multi-GPU serving, and performance tuning
      links:
      - title: Parallelism in TensorRT LLM
        url: https://nvidia.github.io/TensorRT-LLM/features/parallel-strategy.html
        verified_on: '2026-09-05'
      link_status: verified-specific-pages
    fallback:
      resource: vllm-docs
      content: Distributed serving and parallelism documentation
      reason: More approachable open-engine examples.
      links:
      - title: Parallelism and Scaling
        url: https://docs.vllm.ai/en/latest/serving/parallelism_scaling/
        verified_on: '2026-09-05'
      link_status: verified-specific-pages
    exit_criteria:
    - Explain tensor versus pipeline parallelism conceptually.
    - Design placement and autoscaling around memory, topology, queueing, and startup time.
    months:
      start: 13
      end: 14
    sequence: 4
    topics:
    - Tensor parallelism
    - Pipeline parallelism
    - Data parallelism
    - Multi-GPU serving
    - GPU scheduling
  - id: inference-observability-cost
    title: Observability & cost modelling
    effort: medium
    status: not-started
    primary:
      resource: ai-engineering
      content: Chapters 4, 9, and 10 — cost/latency evaluation, optimization, and observability
      link_status: publisher-chapter-links
      links:
      - *id010
      - *id008
      - *id006
    fallback:
      resource: vllm-docs
      content: Metrics, production metrics, and benchmark tooling
      reason: Exposes engine-level measurements to connect with service SLOs.
      links:
      - title: Production Metrics
        url: https://docs.vllm.ai/en/latest/usage/metrics/
        verified_on: '2026-09-05'
      link_status: verified-specific-pages
    exit_criteria:
    - Build a per-request and per-use-case cost model.
    - Connect GPU utilisation, queueing, cache hit rate, and user-perceived latency.
    months:
      start: 12
      end: 14
    sequence: 5
    topics:
    - Serving metrics
    - Latency distributions
    - Token throughput
    - GPU utilization
    - Capacity planning
    - Cost per request
  notes:
  - Close the largest gap between model knowledge and deployable local or private AI.
  - A living curriculum of specific documentation pages, papers and explanations; no single inference textbook is
    implied.
- id: software-architecture
  title: Software & Data Architecture
  code: ARCH
  priority: high
  status: learning
  stage: 1
  months:
    start: 1
    end: 5
  intent: Strengthen boundary, trade-off, dataflow, and distributed-systems judgment.
  depends_on: []
  units:
  - id: architectural-thinking
    title: Architectural thinking & characteristics
    effort: medium
    status: learning
    primary:
      resource: fundamentals-software-architecture
      content: Chapters 1–8 — Introduction through Component-Based Thinking
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/fundamentals-of-software/9781098175504/ch01.html
        title: 1. Introduction
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/fundamentals-of-software/9781098175504/ch02.html
        title: 2. Architectural Thinking
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/fundamentals-of-software/9781098175504/ch03.html
        title: 3. Modularity
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/fundamentals-of-software/9781098175504/ch04.html
        title: 4. Architectural Characteristics Defined
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/fundamentals-of-software/9781098175504/ch05.html
        title: 5. Identifying Architectural Characteristics
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/fundamentals-of-software/9781098175504/ch06.html
        title: 6. Measuring and Governing Architecture Characteristics
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/fundamentals-of-software/9781098175504/ch07.html
        title: 7. The Scope of Architectural Characteristics
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/fundamentals-of-software/9781098175504/ch08.html
        title: 8. Component-Based Thinking
        verified_on: '2026-09-05'
    fallback:
      resource: ddia-2e
      content: Chapters 1–2 — Trade-Offs and Nonfunctional Requirements
      reason: Grounds architectural characteristics in concrete data-system trade-offs.
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html
        title: 1. Trade-Offs in Data Systems Architecture
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html
        title: 2. Defining Nonfunctional Requirements
        verified_on: '2026-09-05'
    exit_criteria:
    - Identify architecturally significant requirements and defend trade-offs.
    - Define component boundaries by responsibility, cohesion, and coupling.
    months:
      start: 1
      end: 2
    sequence: 1
    topics:
    - Architectural characteristics
    - Trade-off analysis
    - Components
    - Boundaries
    - Architecture decisions
  - id: event-driven-architecture
    title: Event-driven architecture
    effort: medium
    status: partially-covered
    primary:
      resource: fundamentals-software-architecture
      content: Chapters 15 and 20 — Event-Driven Architecture Style and Architectural Patterns
      link_status: publisher-chapter-links
      links:
      - &id012
        url: https://www.oreilly.com/library/view/fundamentals-of-software/9781098175504/ch15.html
        title: 15. Event-Driven Architecture Style
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/fundamentals-of-software/9781098175504/ch20.html
        title: 20. Architectural Patterns
        verified_on: '2026-09-05'
    fallback:
      resource: ddia-2e
      content: Chapters 5 and 12 — Encoding and Evolution; Stream Processing
      reason: Stronger treatment of messages, schemas, logs, and correctness.
      link_status: publisher-chapter-links
      links:
      - &id011
        url: https://www.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch05.html
        title: 5. Encoding and Evolution
        verified_on: '2026-09-05'
      - &id014
        url: https://www.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch12.html
        title: 12. Stream Processing
        verified_on: '2026-09-05'
    exit_criteria:
    - Distinguish commands, events, requests, and durable state transitions.
    - Design idempotency, ordering, retry, and failure handling explicitly.
    months:
      start: 2
      end: 3
    sequence: 2
    topics:
    - Events and commands
    - Messaging patterns
    - Delivery semantics
    - Idempotency
    - Event-driven architecture
  - id: data-models-storage
    title: Data models, storage & evolution
    effort: large
    status: learning
    primary:
      resource: ddia-2e
      content: Chapters 3–5 — Data Models, Storage and Retrieval, Encoding and Evolution
      link_status: publisher-chapter-links
      links:
      - &id018
        url: https://www.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html
        title: 3. Data Models and Query Languages
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch04.html
        title: 4. Storage and Retrieval
        verified_on: '2026-09-05'
      - *id011
    fallback:
      resource: fundamentals-software-architecture
      content: Chapter 26 — Architecture and Data Topologies
      reason: Reconnects data choices to the wider application architecture.
      link_status: publisher-chapter-links
      links:
      - &id020
        url: https://www.oreilly.com/library/view/fundamentals-of-software/9781098175504/ch26.html
        title: 26. Architectural Intersections
        verified_on: '2026-09-05'
    exit_criteria:
    - Choose data models and storage around access patterns and evolution.
    - Explain schema compatibility and boundaries between system of record and derived views.
    months:
      start: 2
      end: 3
    sequence: 3
    topics:
    - Data models
    - Storage engines
    - Indexes
    - Encoding
    - Schema evolution
  - id: replication-sharding
    title: Replication & sharding
    effort: large
    status: not-started
    primary:
      resource: ddia-2e
      content: Chapters 6–7 — Replication and Sharding
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch06.html
        title: 6. Replication
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch07.html
        title: 7. Sharding
        verified_on: '2026-09-05'
    fallback:
      resource: fundamentals-software-architecture
      content: Chapters 9 and 18 — Distributed architecture foundations and Microservices
      reason: Reframes data mechanics as architectural style trade-offs.
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/fundamentals-of-software/9781098175504/ch09.html
        title: 9. Foundations
        verified_on: '2026-09-05'
      - &id013
        url: https://www.oreilly.com/library/view/fundamentals-of-software/9781098175504/ch18.html
        title: 18. Microservices Architecture
        verified_on: '2026-09-05'
    exit_criteria:
    - Explain replication lag, conflict handling, hot spots, and rebalancing.
    - Choose partition keys without hand-waving away skew.
    months:
      start: 3
      end: 4
    sequence: 4
    topics:
    - Replication
    - Partitioning
    - Sharding
    - Rebalancing
    - Failure recovery
  - id: transactions-consensus
    title: Transactions, failures & consensus
    effort: large
    status: not-started
    primary:
      resource: ddia-2e
      content: Chapters 8–10 — Transactions, Distributed Systems, Consistency and Consensus
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch08.html
        title: 8. Transactions
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch09.html
        title: 9. The Trouble with Distributed Systems
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch10.html
        title: 10. Consistency and Consensus
        verified_on: '2026-09-05'
    fallback:
      resource: fundamentals-software-architecture
      content: Chapters 15, 18, and 27 — event-driven failure modes, sagas, and architecture laws
      reason: Keeps the theoretical material connected to architecture decisions.
      link_status: publisher-chapter-links
      links:
      - *id012
      - *id013
      - url: https://www.oreilly.com/library/view/fundamentals-of-software/9781098175504/ch27.html
        title: 27. The Laws of Software Architecture, Revisited
        verified_on: '2026-09-05'
    exit_criteria:
    - Reason about partial failure, timeouts, clocks, leases, and idempotency.
    - Explain what a transaction or consensus guarantee does and does not buy.
    months:
      start: 4
      end: 5
    sequence: 5
    topics:
    - Transactions
    - Isolation
    - Distributed failures
    - Consistency
    - Consensus
  - id: batch-stream-dataflow
    title: Batch, streams & derived data
    effort: large
    status: partially-covered
    primary:
      resource: ddia-2e
      content: Chapters 11–13 — Batch Processing, Stream Processing, and Streaming Systems
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch11.html
        title: 11. Batch Processing
        verified_on: '2026-09-05'
      - *id014
      - url: https://www.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch13.html
        title: 13. A Philosophy of Streaming Systems
        verified_on: '2026-09-05'
    fallback:
      resource: designing-ml-systems
      content: Chapters 3 and 10 — dataflow, workflow management, and ML platforms
      reason: Connects dataflow architecture directly to ML lifecycle tooling.
      link_status: publisher-chapter-links
      links:
      - *id015
      - url: https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/ch10.html
        title: 10. Infrastructure and Tooling for MLOps
        verified_on: '2026-09-05'
    exit_criteria:
    - Model source-of-truth, derived state, replay, and exactly-once claims precisely.
    - Choose batch, stream, or hybrid flows for ML jobs and platform events.
    months:
      start: 4
      end: 5
    sequence: 6
    topics:
    - Batch processing
    - Stream processing
    - Event time
    - Materialized views
    - Derived data
  notes:
  - Strengthen boundary, trade-off, dataflow, and distributed-systems judgment.
  - Prioritize boundaries, invariants, state, concurrency, dataflow, and trade-offs. Read DDIA selectively around
    replication, partitioning, transactions, consistency, streams and messaging.
  - A Philosophy of Software Design is optional depth for abstraction and complexity. Clean Code, GoF and Code Complete
    are reference material with no scheduled months.
  - 'AI-assisted coding practice runs alongside the project: review generated code against contracts, invariants,
    tests and architecture decisions.'
  reference_resources:
  - philosophy-software-design
  - clean-code
  - gof
  - code-complete
- id: healthcare-ai
  title: Healthcare AI Integration
  code: CLIN
  priority: very-high
  status: learning
  stage: 4
  months:
    start: 13
    end: 18
  intent: Turn clinical knowledge plus infrastructure skill into safe adoption and integration work.
  depends_on:
  - llm-applications
  - ml-systems
  - inference-systems
  - software-architecture
  units:
  - id: clinical-problem-framing
    title: Clinical problem framing & utility
    effort: large
    status: partially-covered
    primary:
      resource: ai-for-medicine
      content: Diagnosis, prognosis, treatment-effect, and medical-image evaluation material; revisit selectively.
      link_status: specific-source
      links:
      - title: 'Diagnosis: disease detection and model evaluation'
        url: https://www.coursera.org/learn/ai-for-medical-diagnosis
        verified_on: '2026-09-05'
      - title: 'Prognosis: risk and survival models'
        url: https://www.coursera.org/learn/ai-for-medical-prognosis
        verified_on: '2026-09-05'
      - title: 'Treatment: treatment effects and interpretation'
        url: https://www.coursera.org/learn/ai-for-medical-treatment
        verified_on: '2026-09-05'
    fallback:
      resource: designing-ml-systems
      content: Chapter 2 — translating business and task objectives into ML requirements
      reason: Forces the clinical goal to become a deployable system contract.
      link_status: publisher-chapter-links
      links:
      - *id016
    exit_criteria:
    - Define intended use, target population, prediction time, action, comparator, and harm model.
    - Separate statistical performance from clinical utility.
    months:
      start: 13
      end: 13
    sequence: 1
    topics:
    - Clinical utility
    - Target population
    - Outcome definition
    - Decision point
    - Baseline care pathway
  - id: clinical-validation
    title: Clinical validation & monitoring
    effort: large
    status: learning
    primary:
      resource: who-ai-health-guidance
      content: Ethics, governance, accountability, validation, and lifecycle oversight sections
      link_status: chapter-or-lesson-mapping-unverified
    fallback:
      resource: ai-engineering
      content: Chapters 3–4 and 10 — evaluation, systems, monitoring, and feedback
      reason: Converts governance goals into an engineering evaluation loop.
      link_status: publisher-chapter-links
      links:
      - *id017
      - *id010
      - *id006
    exit_criteria:
    - Design retrospective, external, silent-prospective, and monitored rollout phases.
    - Report subgroup, calibration, workflow, safety, and drift evidence.
    months:
      start: 13
      end: 15
    sequence: 2
    topics:
    - Internal validation
    - External validation
    - Calibration
    - Clinical monitoring
    - Lifecycle oversight
  - id: healthcare-interoperability
    title: FHIR, HL7 & clinical data contracts
    effort: large
    status: not-started
    primary:
      resource: hl7-fhir
      content: 'FHIR R4: resource references, REST interactions, terminology bindings and profiling. Work through
        these specific specification pages.'
      links:
      - title: 'FHIR R4: Profiling'
        url: https://hl7.org/fhir/R4/profiling.html
        verified_on: '2026-09-05'
      - title: 'FHIR R4: References'
        url: https://hl7.org/fhir/R4/references.html
        verified_on: '2026-09-05'
      - title: 'FHIR R4: RESTful API'
        url: https://hl7.org/fhir/R4/http.html
        verified_on: '2026-09-05'
      - title: 'FHIR R4: Terminologies'
        url: https://hl7.org/fhir/R4/terminologies.html
        verified_on: '2026-09-05'
      link_status: specific-source
    fallback:
      resource: ddia-2e
      content: Chapters 3 and 5 — data models, encoding, evolution, REST/RPC, and workflows
      reason: Clarifies why a standard still needs explicit boundaries and compatibility strategy.
      link_status: publisher-chapter-links
      links:
      - *id018
      - *id011
    exit_criteria:
    - Read and validate common FHIR resources and profiles.
    - Design mapping, terminology, provenance, and versioning without treating FHIR as a database schema.
    months:
      start: 14
      end: 15
    sequence: 3
    topics:
    - FHIR resources
    - References
    - REST interactions
    - Terminology bindings
    - Profiles and constraints
    - Clinical data contracts
  - id: clinical-imaging-standards
    title: DICOM & multimodal clinical data
    effort: medium
    status: learning
    primary:
      resource: dicom-standard
      content: 'DICOM PS3.5 Chapter 7: data sets, elements and encoding; PS3.18 Chapter 1: DICOMweb scope. Use these
        as the first pass through image metadata and web-service integration.'
      links:
      - title: 'DICOM PS3.18: Web Services scope'
        url: https://dicom.nema.org/medical/dicom/current/output/chtml/part18/chapter_1.html
        verified_on: '2026-09-05'
      - title: 'DICOM PS3.5: Data Sets'
        url: https://dicom.nema.org/medical/dicom/current/output/chtml/part05/chapter_7.html
        verified_on: '2026-09-05'
      link_status: specific-source
    fallback:
      resource: mimic-documentation
      content: 'MIMIC-IV event structure and MIMIC-CXR dataset/file documentation: relate clinical records to imaging
        data.'
      reason: Grounds the standard in the actual multimodal project.
      links:
      - title: MIMIC-IV data documentation
        url: https://mimic.mit.edu/docs/iv/
        verified_on: '2026-09-05'
      - title: 'MIMIC-CXR 2.1.0: dataset and file documentation'
        url: https://physionet.org/content/mimic-cxr/2.1.0/
        verified_on: '2026-09-05'
      link_status: specific-source
    exit_criteria:
    - Explain DICOM objects, metadata, pixel data, and DICOMweb at a systems level.
    - Link tabular, imaging, and encounter data without patient or temporal leakage.
    months:
      start: 15
      end: 16
    sequence: 4
    topics:
    - DICOM data sets
    - Data elements
    - Transfer syntax
    - DICOMweb
    - MIMIC-CXR linkage
    - Multimodal alignment
  - id: eu-regulation-governance
    title: EU regulation & governance
    effort: large
    status: not-started
    primary:
      resource: eu-ai-act
      content: Risk classification, obligations, general-purpose AI context, and implementation timeline
      link_status: chapter-or-lesson-mapping-unverified
    fallback:
      resource: who-ai-health-guidance
      content: Governance and accountability framework
      reason: Broader clinical ethics and institutional responsibilities beyond legal classification.
      link_status: chapter-or-lesson-mapping-unverified
    exit_criteria:
    - Identify likely AI Act, MDR/IVDR, data-protection, and clinical-governance touchpoints.
    - Translate obligations into evidence, controls, monitoring, and named ownership.
    months:
      start: 16
      end: 17
    sequence: 5
    topics:
    - EU AI Act risk classes
    - Provider and deployer obligations
    - Clinical governance
    - Accountability
    - Implementation timeline
  - id: clinical-adoption
    title: Clinical adoption & workflow transformation
    effort: large
    status: not-started
    primary:
      resource: ai-engineering
      content: Chapters 1 and 10 — use-case evaluation, architecture, feedback, and maintenance
      link_status: publisher-chapter-links
      links:
      - *id019
      - *id006
    fallback:
      resource: fundamentals-software-architecture
      content: Chapters 24–26 — teams, negotiation, and intersections with business and implementation
      reason: Adoption fails at organizational boundaries as often as at model boundaries.
      link_status: publisher-chapter-links
      links:
      - url: https://www.oreilly.com/library/view/fundamentals-of-software/9781098175504/ch24.html
        title: 24. Making Teams Effective
        verified_on: '2026-09-05'
      - url: https://www.oreilly.com/library/view/fundamentals-of-software/9781098175504/ch25.html
        title: 25. Negotiation and Leadership Skills
        verified_on: '2026-09-05'
      - *id020
    exit_criteria:
    - Map actors, workflow, failure recovery, escalation, and feedback before choosing the AI pattern.
    - Run a technical-clinical discovery conversation and turn it into testable requirements.
    months:
      start: 17
      end: 18
    sequence: 6
    topics:
    - Workflow integration
    - User feedback
    - Human factors
    - Change management
    - Post-deployment maintenance
  notes:
  - Turn clinical knowledge plus infrastructure skill into safe adoption and integration work.
resources:
- id: mathematics-for-ml
  title: Mathematics for Machine Learning
  type: book
  authors:
  - Marc Peter Deisenroth
  - A. Aldo Faisal
  - Cheng Soon Ong
  year: 2020
  role: primary
  source_url: https://mml-book.com/
  source_label: Official companion site
  toc_status: verified
  contents:
  - 1. Introduction and Motivation
  - 2. Linear Algebra
  - 3. Analytic Geometry
  - 4. Matrix Decompositions
  - 5. Vector Calculus
  - 6. Probability and Distribution
  - 7. Continuous Optimization
  - 8. When Models Meet Data
  - 9. Linear Regression
  - 10. Dimensionality Reduction with Principal Component Analysis
  - 11. Density Estimation with Gaussian Mixture Models
  - 12. Classification with Support Vector Machines
  area_ids:
  - mathematics
- id: professor-leonard-calculus
  title: Professor Leonard Mathematics Courses
  type: video-series
  authors:
  - Professor Leonard
  role: primary
  source_url: https://www.youtube.com/@ProfessorLeonard/courses
  source_label: Official course channel
  toc_status: course-level
  contents:
  - Precalculus — functions, graphs, exponentials, logarithms, and trigonometry
  - Calculus I — limits, derivatives, applications, integrals
  - Calculus II — integration techniques, sequences and series, parametric and polar topics
  - Calculus III — vectors, partial derivatives, multiple integrals, vector calculus
  area_ids:
  - mathematics
- id: mit-1806-linear-algebra
  title: MIT 18.06 Linear Algebra
  type: course
  authors:
  - Gilbert Strang
  role: fallback
  source_url: https://ocw.mit.edu/courses/18-06-linear-algebra-spring-2010/
  source_label: MIT OpenCourseWare
  toc_status: verified-course
  contents:
  - The geometry of linear equations
  - Elimination with matrices
  - Vector spaces and subspaces
  - Orthogonality and projections
  - Determinants
  - Eigenvalues and eigenvectors
  - Positive definite matrices
  - Singular value decomposition
  - Linear transformations and applications
  area_ids:
  - mathematics
- id: aggarwal-linear-algebra
  title: Linear Algebra and Optimization for Machine Learning
  type: book
  authors:
  - Charu C. Aggarwal
  year: 2026
  edition: 2
  role: depth
  source_url: https://link.springer.com/book/10.1007/978-3-031-98619-2
  source_label: Springer Nature
  toc_status: verified
  contents:
  - 1. Linear Algebra and Optimization — An Introduction
  - 2. Linear Transformations and Linear Systems
  - 3. Eigenvectors and Diagonalizable Matrices
  - 4. Optimization Basics — A Machine Learning View
  - 5. Advanced Optimization Solutions
  - 6. Constrained Optimization and Duality
  - 7. Singular Value Decomposition
  - 8. Matrix Factorization
  - 9. The Linear Algebra of Similarity
  - 10. The Linear Algebra of Graphs
  - 11. Optimization in Computational Graphs
  area_ids:
  - mathematics
- id: matrix-calculus-dl
  title: The Matrix Calculus You Need for Deep Learning
  type: paper
  authors:
  - Terence Parr
  - Jeremy Howard
  year: 2018
  role: primary
  source_url: https://arxiv.org/abs/1802.01528
  source_label: arXiv author manuscript
  toc_status: section-level
  contents:
  - Scalar derivatives
  - Vector calculus and partial derivatives
  - Matrix calculus identities
  - Neural-network gradient derivation
  area_ids:
  - mathematics
- id: stat110
  title: Harvard Statistics 110 — Probability
  type: course
  authors:
  - Joseph K. Blitzstein
  role: primary
  source_url: https://stat110.hsites.harvard.edu/
  source_label: Harvard course site
  toc_status: verified-course
  contents:
  - Counting and story proofs
  - Conditional probability and Bayes' rule
  - Discrete and continuous random variables
  - Expectation, variance, covariance, and transforms
  - Common distributions and their relationships
  - Joint, conditional, and multivariate distributions
  - Law of large numbers and central limit theorem
  - Markov chains
  area_ids:
  - mathematics
- id: islp
  title: An Introduction to Statistical Learning — with Applications in Python
  type: book
  authors:
  - Gareth James
  - Daniela Witten
  - Trevor Hastie
  - Robert Tibshirani
  - Jonathan Taylor
  year: 2023
  role: primary
  source_url: https://www.statlearning.com/
  source_label: Official book site
  toc_status: verified
  contents:
  - 1. Introduction
  - 2. Statistical Learning
  - 3. Linear Regression
  - 4. Classification
  - 5. Resampling Methods
  - 6. Linear Model Selection and Regularization
  - 7. Moving Beyond Linearity
  - 8. Tree-Based Methods
  - 9. Support Vector Machines
  - 10. Deep Learning
  - 11. Survival Analysis and Censored Data
  - 12. Unsupervised Learning
  - 13. Multiple Testing
  area_ids:
  - classical-ml
  - mathematics
- id: geron-pytorch
  title: Hands-On Machine Learning with Scikit-Learn and PyTorch
  type: book
  authors:
  - Aurélien Géron
  year: 2025
  role: primary
  source_url: https://www.oreilly.com/library/view/hands-on-machine-learning/9798341607972/
  source_label: O'Reilly
  toc_status: verified
  contents:
  - 1. The Machine Learning Landscape
  - 2. End-to-End Machine Learning Project
  - 3. Classification
  - 4. Training Models
  - 5. Decision Trees
  - 6. Ensemble Learning and Random Forests
  - 7. Dimensionality Reduction
  - 8. Unsupervised Learning Techniques
  - 9. Introduction to Artificial Neural Networks
  - 10. Building Neural Networks with PyTorch
  - 11. Training Deep Neural Networks
  - 12. Deep Computer Vision Using Convolutional Neural Networks
  - 13. Processing Sequences Using RNNs and CNNs
  - 14. Natural Language Processing with RNNs and Attention
  - 15. Transformers for Natural Language Processing and Chatbots
  - 16. Vision and Multimodal Transformers
  - 17. Speeding Up Transformers
  - 18. Autoencoders, GANs, and Diffusion Models
  - 19. Reinforcement Learning
  - Appendix A. Autodiff
  - Appendix B. Mixed Precision and Quantization
  area_ids:
  - classical-ml
  - deep-learning
  - inference-systems
  - ml-systems
- id: understanding-deep-learning
  title: Understanding Deep Learning
  type: book
  authors:
  - Simon J. D. Prince
  year: 2023
  role: primary
  source_url: https://mitpress.mit.edu/9780262048644/understanding-deep-learning/
  source_label: MIT Press
  toc_status: verified
  contents:
  - 1. Introduction
  - 2. Supervised Learning
  - 3. Shallow Neural Networks
  - 4. Deep Neural Networks
  - 5. Loss Functions
  - 6. Fitting Models
  - 7. Gradients and Initialization
  - 8. Measuring Performance
  - 9. Regularization
  - 10. Convolutional Networks
  - 11. Residual Networks
  - 12. Transformers
  - 13. Graph Neural Networks
  - 14. Unsupervised Learning
  - 15. Generative Adversarial Networks
  - 16. Normalizing Flows
  - 17. Variational Autoencoders
  - 18. Diffusion Models
  - 19. Reinforcement Learning
  - 20. Why Does Deep Learning Work?
  - 21. Deep Learning and Ethics
  - Appendix A. Notation
  - Appendix B. Mathematics
  - Appendix C. Probability
  area_ids:
  - deep-learning
  - mathematics
- id: nn-zero-to-hero
  title: Neural Networks — Zero to Hero
  type: video-series
  authors:
  - Andrej Karpathy
  role: fallback
  source_url: https://github.com/karpathy/nn-zero-to-hero
  source_label: Official course repository
  toc_status: verified-course
  contents:
  - 1. Neural networks and backpropagation — building micrograd
  - 2. Language modelling — building makemore
  - 3. MLPs, model training, splits, underfitting and overfitting
  - 4. Activations, gradients, and BatchNorm
  - 5. Becoming a Backprop Ninja
  - 6. Building WaveNet
  - 7. Build GPT from scratch
  - 8. Build the GPT Tokenizer
  area_ids:
  - deep-learning
  - llm-applications
- id: hands-on-llms
  title: Hands-On Large Language Models
  type: book
  authors:
  - Jay Alammar
  - Maarten Grootendorst
  year: 2024
  role: primary
  source_url: https://www.oreilly.com/library/view/hands-on-large-language/9781098150952/
  source_label: O'Reilly
  toc_status: verified
  contents:
  - 1. An Introduction to Large Language Models
  - 2. Tokens and Embeddings
  - 3. Looking Inside Large Language Models
  - 4. Text Classification
  - 5. Text Clustering and Topic Modeling
  - 6. Prompt Engineering
  - 7. Advanced Text Generation Techniques and Tools
  - 8. Semantic Search and Retrieval-Augmented Generation
  - 9. Multimodal Large Language Models
  - 10. Creating Text Embedding Models
  - 11. Fine-Tuning Representation Models for Classification
  - 12. Fine-Tuning Generation Models
  area_ids:
  - inference-systems
  - llm-applications
- id: designing-ml-systems
  title: Designing Machine Learning Systems
  type: book
  authors:
  - Chip Huyen
  year: 2022
  role: primary
  source_url: https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/
  source_label: O'Reilly
  toc_status: verified
  contents:
  - 1. Overview of Machine Learning Systems
  - 2. Introduction to Machine Learning Systems Design
  - 3. Data Engineering Fundamentals
  - 4. Training Data
  - 5. Feature Engineering
  - 6. Model Development and Offline Evaluation
  - 7. Model Deployment and Prediction Service
  - 8. Data Distribution Shifts and Monitoring
  - 9. Continual Learning and Test in Production
  - 10. Infrastructure and Tooling for MLOps
  - 11. The Human Side of Machine Learning
  area_ids:
  - healthcare-ai
  - ml-systems
  - software-architecture
- id: ai-engineering
  title: AI Engineering
  type: book
  authors:
  - Chip Huyen
  year: 2024
  role: primary
  source_url: https://www.oreilly.com/library/view/ai-engineering/9781098166298/
  source_label: O'Reilly
  toc_status: verified
  contents:
  - 1. Introduction to Building AI Applications with Foundation Models
  - 2. Understanding Foundation Models
  - 3. Evaluation Methodology
  - 4. Evaluate AI Systems
  - 5. Prompt Engineering
  - 6. RAG and Agents
  - 7. Finetuning
  - 8. Dataset Engineering
  - 9. Inference Optimization
  - 10. AI Engineering Architecture and User Feedback
  area_ids:
  - healthcare-ai
  - inference-systems
  - llm-applications
  - ml-systems
- id: ddia-2e
  title: Designing Data-Intensive Applications
  type: book
  authors:
  - Martin Kleppmann
  - Chris Riccomini
  year: 2026
  edition: 2
  role: primary-selective
  source_url: https://www.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/
  source_label: O'Reilly
  toc_status: verified
  contents:
  - 1. Trade-Offs in Data Systems Architecture
  - 2. Defining Nonfunctional Requirements
  - 3. Data Models and Query Languages
  - 4. Storage and Retrieval
  - 5. Encoding and Evolution
  - 6. Replication
  - 7. Sharding
  - 8. Transactions
  - 9. The Trouble with Distributed Systems
  - 10. Consistency and Consensus
  - 11. Batch Processing
  - 12. Stream Processing
  - 13. A Philosophy of Streaming Systems
  - 14. Doing the Right Thing
  area_ids:
  - healthcare-ai
  - software-architecture
- id: fundamentals-software-architecture
  title: Fundamentals of Software Architecture
  type: book
  authors:
  - Mark Richards
  - Neal Ford
  year: 2025
  edition: 2
  role: primary-selective
  source_url: https://www.oreilly.com/library/view/fundamentals-of-software/9781098175504/
  source_label: O'Reilly
  toc_status: verified
  contents:
  - 1. Introduction
  - 2. Architectural Thinking
  - 3. Modularity
  - 4. Architectural Characteristics Defined
  - 5. Identifying Architectural Characteristics
  - 6. Measuring and Governing Architecture Characteristics
  - 7. The Scope of Architectural Characteristics
  - 8. Component-Based Thinking
  - 9. Foundations
  - 10. Layered Architecture Style
  - 11. The Modular Monolith Architecture Style
  - 12. Pipeline Architecture Style
  - 13. Microkernel Architecture Style
  - 14. Service-Based Architecture Style
  - 15. Event-Driven Architecture Style
  - 16. Space-Based Architecture Style
  - 17. Orchestration-Driven Service-Oriented Architecture
  - 18. Microservices Architecture
  - 19. Choosing the Appropriate Architecture Style
  - 20. Architectural Patterns
  - 21. Architectural Decisions
  - 22. Analyzing Architecture Risk
  - 23. Diagramming Architecture
  - 24. Making Teams Effective
  - 25. Negotiation and Leadership Skills
  - 26. Architectural Intersections
  - 27. The Laws of Software Architecture, Revisited
  area_ids:
  - healthcare-ai
  - software-architecture
- id: vllm-docs
  title: vLLM Documentation
  type: documentation
  authors:
  - vLLM project
  role: living-primary
  source_url: https://docs.vllm.ai/
  source_label: Official documentation
  toc_status: living
  contents:
  - Architecture and engine internals
  - PagedAttention and KV cache
  - OpenAI-compatible serving
  - Distributed serving and parallelism
  - Metrics, benchmarking, and optimisation
  area_ids:
  - inference-systems
- id: sglang-docs
  title: SGLang Documentation
  type: documentation
  authors:
  - SGLang project
  role: living-fallback
  source_url: https://docs.sglang.ai/
  source_label: Official documentation
  toc_status: living
  contents:
  - Runtime and server
  - Scheduling and prefix caching
  - Quantization and parallelism
  - Observability and benchmarking
  area_ids: []
- id: tensorrt-llm-docs
  title: TensorRT-LLM Documentation
  type: documentation
  authors:
  - NVIDIA
  role: living-depth
  source_url: https://nvidia.github.io/TensorRT-LLM/
  source_label: Official documentation
  toc_status: living
  contents:
  - Architecture and supported models
  - Precision and quantization
  - Parallelism and multi-GPU serving
  - Performance tuning and benchmarking
  area_ids:
  - inference-systems
- id: ai-for-medicine
  title: AI for Medicine Specialization
  type: course
  authors:
  - DeepLearning.AI
  role: completed-reference
  source_url: https://www.coursera.org/specializations/ai-for-medicine
  source_label: Official course page
  toc_status: course-level
  contents:
  - AI for Medical Diagnosis
  - AI for Medical Prognosis
  - AI for Medical Treatment
  area_ids:
  - healthcare-ai
- id: who-ai-health-guidance
  title: Ethics and Governance of Artificial Intelligence for Health
  type: report
  authors:
  - World Health Organization
  year: 2021
  role: primary
  source_url: https://www.who.int/publications/i/item/9789240029200
  source_label: WHO
  toc_status: verified-report
  contents:
  - AI opportunities and challenges in health
  - Laws, policies, and principles
  - Ethical challenges to use of AI for health
  - Accountability and governance
  area_ids:
  - healthcare-ai
- id: hl7-fhir
  title: HL7 FHIR Specification
  type: documentation
  authors:
  - HL7 International
  role: living-primary
  source_url: https://hl7.org/fhir/
  source_label: Official specification
  toc_status: living
  contents:
  - Foundation and implementation support
  - Resources and data types
  - Conformance, profiling, and terminology
  - Exchange, REST, messaging, documents, and services
  - Privacy, security, and clinical safety
  area_ids:
  - healthcare-ai
- id: dicom-standard
  title: DICOM Standard
  type: standard
  authors:
  - DICOM Standards Committee
  role: living-primary
  source_url: https://www.dicomstandard.org/current
  source_label: Official standard
  toc_status: living
  contents:
  - Concepts and overview
  - Information object definitions
  - Service class specifications
  - Data structures and encoding
  - Network communication
  - Media storage and DICOMweb
  area_ids:
  - healthcare-ai
- id: eu-ai-act
  title: EU Artificial Intelligence Act
  type: regulation
  authors:
  - European Union
  role: living-primary
  source_url: https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
  source_label: European Commission
  toc_status: living
  contents:
  - Risk-based framework
  - High-risk system obligations
  - Transparency and general-purpose AI
  - Governance, enforcement, and implementation timeline
  area_ids:
  - healthcare-ai
- id: mimic-documentation
  title: MIMIC Clinical Database Documentation
  type: documentation
  authors:
  - MIT Laboratory for Computational Physiology
  role: applied-reference
  source_url: https://physionet.org/content/mimiciv/
  source_label: PhysioNet
  toc_status: living
  contents:
  - MIMIC-IV hospital and ICU data
  - MIMIC-ED emergency-department data
  - MIMIC-CXR imaging data and metadata
  - Credentialing and data-use requirements
  area_ids:
  - healthcare-ai
- id: philosophy-software-design
  title: A Philosophy of Software Design
  type: book
  authors:
  - John Ousterhout
  role: optional-depth
  area_ids:
  - software-architecture
  toc_status: not-captured
  contents: []
  usage: Optional depth on complexity and abstraction boundaries; return when a real design decision needs it.
- id: clean-code
  title: Clean Code
  type: book
  authors:
  - Robert C. Martin
  role: reference
  area_ids:
  - software-architecture
  toc_status: not-captured
  contents: []
- id: gof
  title: Design Patterns
  type: book
  authors:
  - Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides
  role: reference
  area_ids:
  - software-architecture
  toc_status: not-captured
  contents: []
- id: code-complete
  title: Code Complete
  type: book
  authors:
  - Steve McConnell
  role: reference
  area_ids:
  - software-architecture
  toc_status: not-captured
  contents: []
- id: calculus-lifesaver
  title: The Calculus Lifesaver
  type: book
  authors:
  - Adrian Banner
  role: supplementary
  area_ids:
  - mathematics
  toc_status: not-captured
  contents: []
  usage: Second explanations and exercises when Leonard does not click; not a parallel full course.
- id: bishop-prml
  title: Pattern Recognition and Machine Learning
  authors:
  - Christopher M. Bishop
  type: book
  role: reference
  toc_status: not-captured
  contents: []
  area_ids:
  - classical-ml
  usage: Unscheduled theoretical reference; consult for a specific gap after the practical first pass.
- id: understanding-ml
  title: 'Understanding Machine Learning: From Theory to Algorithms'
  authors:
  - Shai Shalev-Shwartz and Shai Ben-David
  type: book
  role: reference
  toc_status: not-captured
  contents: []
  area_ids:
  - classical-ml
  usage: Unscheduled theoretical reference; consult for a specific gap after the practical first pass.
dropped:
  resources:
  - title: Numerical Linear Algebra
    type: book
    author: Lloyd N. Trefethen and David Bau III
    former_area: Mathematics
    reason: Excellent advanced material, but numerical-linear-algebra depth has low immediate ROI for the target
      role.
    reconsider_when:
    - Numerical stability becomes a recurring problem
    - Scientific ML becomes relevant
    - Low-level optimisation becomes a specialization
  - title: The Elements of Statistical Learning
    type: book
    author: Trevor Hastie, Robert Tibshirani, Jerome Friedman
    former_area: Classical Machine Learning
    reason: Too derivation-heavy to justify a scheduled place when ISLP covers the required statistical learning
      path.
    alternative: An Introduction to Statistical Learning — with Applications in Python
  - title: Probabilistic Machine Learning
    type: book
    author: Kevin P. Murphy
    former_area: Machine Learning
    reason: Valuable depth reference, but too broad to become a scheduled core curriculum now.
    reconsider_when:
    - Probabilistic modelling becomes central to a project
    - Research-level depth is required
  - title: Practical MLOps
    type: book
    former_area: ML Systems
    reason: Duplicates infrastructure and delivery knowledge already demonstrated; the scarce material is ML-specific
      lifecycle judgment.
    alternative: Designing Machine Learning Systems
  - title: Introductory Statistics for the Life and Biomedical Sciences
    type: book
    former_area: Mathematics
    reason: Not required by this roadmap. ISLP is the agreed main statistical-ML bridge; a separate biostatistics
      curriculum would duplicate and redirect that path.
    alternative: ISLP
  patterns:
  - title: Duplicate introductory ML courses
    reason: The Machine Learning Specialization is already complete.
  - title: Duplicate introductory deep-learning courses
    reason: The Deep Learning Specialization is already complete.
  - title: Full data-structures-and-algorithms curriculum
    reason: No scheduled months; study interview- or project-relevant gaps just in time.
  - title: General programming-language study
    reason: Learn language features when a project requires them rather than as a parallel formal track.
  - title: Clean Code and GoF as formal curricula
    reason: Keep as historical references; prioritize boundaries, invariants, dataflow, and architectural trade-offs.
  - title: Broad statistics bootcamps
    reason: ISLP plus targeted probability study provides the agreed statistical-ML bridge. Do not schedule another
      broad introductory curriculum.
