Labs

Labs#

Seventeen Jupyter notebooks released across the semester in three chunks. Each lab is a self-contained exercise in a particular ML model type, loss type, or data type. The labs were originally distributed as Colab-runnable notebooks and were the substrate for three in-class lab days (L11, L12, L16).

The release schedule:

Chunk 1 — ML model types (released Sun Feb 22, 2026, used for Lab Day #1 and Lab Day #2).
Chunk 2 — ML loss types / objectives (released Mon Mar 2, 2026, used in Lab Day #3).
Chunk 3 — Data types / modalities (released Fri Mar 6, 2026, used in Lab Day #3).

Students worked the labs in order during the semester. The labs in this book are the solution versions — every TODO is filled in and every code cell has run with its outputs visible. Use them to study: read the prompts, then read the worked solutions, then run them in Colab to experiment.

Click any lab below to view it inline. To re-run it yourself, use the launch button at the top right of any notebook page (Open in Colab / Open in Binder).

Note. For chunk 1 (labs 0-5) the solutions are a separate notebook — the version shown here. For chunks 2-3 (labs 6-16) the original released notebooks already include solution cells inline at the bottom of each section, marked “Solution: …”.

What each lab covers#

Chunk 1 — ML model types#

Lab	Topic	Connects to
Lab 0	Preprocessing, EDA, missing data, scaling, train/val/test splits	Foundation for every other lab
Lab 1	Logistic regression on the breast-cancer dataset	L9 (binary-classification training)
Lab 2	Decision trees; interpretability vs. overfitting	L10 (capacity / regularization)
Lab 3	k-Nearest Neighbors; bias-variance via k	L9 (kNN consistency)
Lab 4	Random forests and gradient boosting	L10 (variance reduction)
Lab 5	First feedforward neural network	L13 (NN lecture)

Chunk 2 — ML loss types / objectives#

Lab	Topic	Connects to
Lab 6	Going from binary to K-way; softmax + cross-entropy	L4 (information theory), L7 (NLL)
Lab 7	Squared error, regression metrics, prediction intervals	L7 (Gaussian NLL)
Lab 8	Cox proportional hazards as NLL with censoring	L23 (population health, survival)
Lab 9	Predicting distributions, calibration	L8 (calibration vs. discrimination)
Lab 10	k-means, GMMs, the unsupervised side of L7	L7 (mixture models)
Lab 11	PCA, t-SNE, UMAP	L2 (eigenanalysis), L25 (population PCA)

Chunk 3 — Data types / modalities#

Lab	Topic	Connects to
Lab 12	Graph neural networks, message passing	L26-L27 (molecules, biological AI)
Lab 13	Sequence inputs; RNN/transformer architectures	L14 (LLMs)
Lab 14	Convolutional networks on images	L21-L22 (medical imaging)
Lab 15	Irregular sampling, missingness, time-series losses	L17-L18 (EHR data)
Lab 16	Health event streams in MEDS format	L17-L18 (EHR data)

How to run the labs#

Each lab notebook has Colab-friendly boilerplate at the top. To run a lab:

Open the notebook page in this book (links above).
Click the launch icon at the top right of the notebook page; it opens the notebook in Colab with one click.
Run cells in order.

Runtime expectations#

Most labs run comfortably on a free Colab CPU runtime in under 10 minutes. The exceptions:

Lab	Approx. runtime	Free CPU OK?	Free GPU helpful?	Notes
Lab 0-5	1-3 min each	yes	no	Pure scikit-learn / numpy / matplotlib.
Lab 5 (NN)	3-5 min	yes	no	Small NN; no GPU needed.
Lab 6-9	1-3 min each	yes	no	Loss-type variants; classical methods.
Lab 10-11	2-5 min each	yes	no	Clustering / dim-reduction; UMAP install can take ~30s.
Lab 12 (graphs)	5-15 min	yes	recommended	Installs `torch_geometric` and trains a small GCN; faster on GPU.
Lab 13 (sequences)	10-30 min	acceptable	recommended	Fine-tunes a small transformer; CPU works but is slow.
Lab 14 (images)	10-30 min	acceptable	recommended	CNN training on MedMNIST; downloads ~80 MB of data.
Lab 15 (time series)	5-15 min	yes	no	`wfdb` data downloads can take a minute.
Lab 16 (MEDS)	5-15 min	yes	no	Downloads small MIMIC-IV demo data in MEDS format.

Cross-lab dependencies#

Labs 0-11 share a processed_data.pkl produced by Lab 0. Run Lab 0 first (or download its output) before opening labs 1-11. Labs 12-16 are independent of that pickle but each needs its own dataset download (graph data, sequence corpus, MedMNIST, ECG signals, MIMIC-IV demo respectively).

If you’re reading this book locally, you can also open any lab directly in Jupyter / VS Code from Labs/notebooks/. The Python environment used to render this book in CI is documented in pyproject.toml (Jupyter Book itself); per-notebook scientific dependencies are documented in scripts/notebook_extras.tsv and are installed into ephemeral isolated venvs by scripts/execute_notebook.sh during book builds.