Lecture 16 — Lab Day #3 (last class before spring break)

Lecture 16 — Lab Day #3 (last class before spring break)#

⚠️ AI-synthesized; not fully reviewed by course staff. Treat as a study aid; released slides, notebooks, and lecture recordings are authoritative.

Thu Mar 12, 2026 · Part 2 — Modern AI & lab work

What this class meeting was about#

Third and final lab day of the semester, last class before spring break. By this point students had access to all three lab chunks:

  • Chunk 1: ML model types (labs 0-5, released Sun Feb 22) — covered in lab days #1 and #2.

  • Chunk 2: ML loss types (labs 6-11, released Mon Mar 2) — multiclass, regression, survival, probabilistic, clustering, dimensionality reduction.

  • Chunk 3: Data types (labs 12-16, released Fri Mar 6) — graphs, sequences, images, time series, event streams.

Most students came in with chunk 1 finished or nearly so, working through chunks 2 and 3 in order. This lab day’s folder includes labs 6-16 (the chunks that were new on this day; chunk 1 lives in lecture-11-lab-day-1/labs/).

The new labs in scope:

  • Chunk 2 — losses.

    • lab6_multiclass_classification — going from binary to K-way; softmax + cross-entropy.

    • lab7_regression — squared error, regression metrics, prediction intervals.

    • lab8_survival_analysis — Cox proportional hazards as an NLL with censoring; preview of L23.

    • lab9_probabilistic_regression — predicting distributions, not just point estimates; calibration.

    • lab10_clustering — k-means, GMMs, the unsupervised side of L7’s recipe.

    • lab11_dimensionality_reduction — PCA, t-SNE, UMAP; the representation question explicit.

  • Chunk 3 — data types.

    • lab12_graph_data — graph neural networks, message passing, common pitfalls.

    • lab13_ordinal_sequences — sequence inputs, RNN/transformer architectures (preview of L14).

    • lab14_images — convolutional networks on images (preview of L21-L22).

    • lab15_timeseries — irregular sampling, missingness, time-series-specific losses.

    • lab16_event_streams_MEDS — health event streams in MEDS format (preview of L17-L18).

Why this lab day matters for the course#

Three roles for this slot:

It bridges the foundations half and the health-data half. The labs touch every data type that the modality lectures (L17-L22, L23-L27) build on. Wrestling with the generic versions before the clinical versions saves a lot of cognitive load later.

It introduces survival analysis (lab 8) and event streams (lab 16) as warm-ups. L23 and L17-L18 will go deep on these in clinical contexts; the labs give you the generic skeleton first, so the clinical version is “what’s different about EHR” rather than “what’s a Cox model.”

It demonstrates the loss / model / data-type orthogonality. The same logistic-regression-style training loop adapts to multiclass, regression, survival — by changing the loss. The same architecture template adapts to images, sequences, graphs — by changing the input encoding. This orthogonality is the biggest single take-away of the lab program.

What you should walk away with#

  • A mental table that crosses loss type (binary, multiclass, regression, survival, probabilistic) with model type (linear, tree, NN). Almost every cell is feasible; the ones that aren’t are interesting.

  • A working sense of when “image” / “sequence” / “graph” / “event stream” each forces a particular architectural choice and when it doesn’t.

  • First-hand experience with calibration on a probabilistic regressor (lab 9) — connecting back to L8.

  • An intuition for survival analysis that L23 will formalize.

  • Familiarity with the MEDS event-stream schema, which L17-L18 will build on.

How this connects to the rest of the course#

  • L17-L18 (EHR / claims) — lab16_event_streams_MEDS is the on-ramp.

  • L21-L22 (medical imaging) — lab14_images is the on-ramp.

  • L23 (population health, survival) — lab8_survival_analysis is the on-ramp.

  • L24 (causality, fairness) — lab9_probabilistic_regression and lab11_dimensionality_reduction provide the building blocks.

  • L25-L26 (DNA, proteins) — lab12_graph_data and lab13_ordinal_sequences provide the substrate.

Study guide#

Key terms, self-check questions, and additional resources for active recall.

Chunk 2 — ML Loss Types / Objectives#

Lab

Loss / Task

Probabilistic Model

lab6 — Multiclass classification

Categorical cross-entropy

Categorical / Multinomial; softmax output.

lab7 — Regression

MSE (default), MAE

Gaussian; Laplace.

lab8 — Survival analysis

Cox partial likelihood

Proportional-hazards model with right-censoring.

lab9 — Probabilistic regression

Gaussian NLL with predicted variance

Heteroscedastic Gaussian. Calibration becomes explicit.

lab10 — Clustering

Within-cluster sum of squares (k-means); GMM NLL

k-means; Gaussian mixture.

lab11 — Dimensionality reduction

PCA reconstruction error; t-SNE / UMAP objectives

Linear (PCA) vs. neighborhood-preserving (t-SNE/UMAP).

Chunk 3 — Data Types / Modalities#

Lab

Data Shape

Architectural Implication

lab12 — Graph data

Nodes + edges

Graph neural networks; message passing.

lab13 — Ordinal sequences

Tokens with order

RNN / transformer encoder.

lab14 — Images

2D pixel grids

CNN with translation equivariance.

lab15 — Time series

Irregular timestamps + values

Time-series-specific losses, missingness handling.

lab16 — Event streams (MEDS)

Health event streams

Set-of-irregular-events transformer or chunked RNN. (Preview of L17-L18.)

Things to Practice#

The orthogonality of model / loss / data

The same logistic-regression-style training loop adapts to multiclass, regression, survival — by changing the loss. The same architectural template adapts to images, sequences, graphs — by changing the input encoding. Loss type and data type are independent ML axes; both matter.

Self-Check Questions#

  1. Why does survival analysis use Cox partial likelihood instead of just MSE on time-to-event? (Hint: censoring.)

  2. A probabilistic regressor (lab 9) outputs both a mean and a variance. Why is this useful clinically? What’s the calibration check?

  3. PCA, t-SNE, and UMAP all reduce dimensions. What does each preserve? Which would you trust to claim “this 2D embedding shows the actual structure of the data”?

  4. A whole-slide pathology image is 50,000 × 50,000 pixels. Why can’t you run a CNN end-to-end on it, and what’s the standard workaround?

  5. MEDS-format event streams (lab 16) preserve event timestamps. Why is “discarding timestamps and tabularizing” a real loss, not just an inconvenience?

Additional Resources#