Schedule

The schedule is tentative and will be updated as the term progresses. Complete each week’s reading before Tuesday’s lecture unless otherwise announced.

Theme 1 · Data, Python, Modeling, and Optimization

Date	Topic	Reading	Assignments
Tue, Sep 1, 2026	Lecture 1 What can we learn from data? Crash course on Python [slides] [handout]	MML, Chapter 1	HW01: Computing and plotting in Python Due: 2026-09-08
Thu, Sep 3, 2026	Lecture 2 Representing data Introduction to modeling: what is a model? [slides] [handout]	MML, §§8.1 and 8.2.1–8.2.3
Tue, Sep 8, 2026	Lecture 3 Linear and polynomial models [slides] [handout]	Daumé, §§7.1–7.6 Optional: MML, §§2.1–2.2, 2.5, and 2.7.1	HW02: Modeling climate change Due: 2026-09-14
Thu, Sep 10, 2026	Lecture 4 Vectors, matrices, data matrices, and matrix operations [slides] [handout]	Vector and Matrix handout
Tue, Sep 15, 2026	Lecture 5 Multiple Linear regression, gradients, and gradient descent [slides] [handout]	MML, Chapters 7–7.1 and 9–9.2 Handout 5 solution	HW03: Gradient descent Due: 2026-09-22

Theme 2 · Model Evaluation, Probability, and Information

Date	Topic	Reading	Assignments
Thu, Sep 17, 2026	Lecture 6 Classification Confusion matrices; precision, recall, specificity, and sensitivity [slides] [handout]	Manning et al., Evaluation in Information Retrieval, §§8.3–8.4 Speech and Language Processing (3rd ed. draft) by Jurafsky & Martin: Chapter 4.9
Tue, Sep 22, 2026	Evaluation and an introduction to probability	MML, §§6.1–6.3 Daumé, §§9.1–9.4	Evaluation metrics
Thu, Sep 24, 2026	Bayes’ rule and probabilistic models
Tue, Sep 29, 2026	Naive Bayes, conditional independence, and probabilistic classification	Daumé, §§9.1–9.4	Naive Bayes
Thu, Oct 1, 2026	Catch up/Review
Tue, Oct 6, 2026	Midterm 1 review
Thu, Oct 8, 2026	Midterm 1
Tue, Oct 13, 2026	Fall Break — no class or hw
Thu, Oct 15, 2026	Fall Break — no class
Tue, Oct 20, 2026	Information, entropy, coding theory, and applications in machine learning	MIT notes, Information, Entropy, and the Motivation for Source Codes Understanding Entropy
Thu, Oct 22, 2026	Logistic regression and principles of data visualization	Fundamentals of Data Visualization, Chapters 1–5, 18, and 22 Optional: Mitchell, Generative and Discriminative Classifiers, §3
Tue, Oct 27, 2026	EMNLP Conference - no class		Logistic regression and visualization
Thu, Oct 29, 2026	Visual encodings, graph visualization, and PCA	MML, Chapter 10; Daumé, Chapter 15 (PCA)

Theme 4 · Statistical Inference

Date	Topic	Reading	Assignments
Tue, Nov 3, 2026	Normal distributions, hypothesis testing, and p-values	Crash Course Statistics, episodes 18–21, 26, and 27	Statistics and visualization
Thu, Nov 5, 2026	Randomized trials, permutation tests, and t-tests
Tue, Nov 10, 2026	Confidence intervals and the bootstrap	Bootstrap demonstration by Sara Mathieson	Bootstrap and confidence intervals
Thu, Nov 12, 2026	Bagging and random forests

Theme 5 · Unsupervised Learning

Date	Topic	Reading	Assignments
Tue, Nov 17, 2026	Dimensionality reduction: PCA and t-SNE	Daumé, Chapter 15 (K-means)	Dimensionality reduction and clustering
Thu, Nov 19, 2026	Midterm 2
Tue, Nov 24, 2026	Unsupervised Learning II: K-means, Gaussian mixture models, and KDE	MML, Chapter 11 (Gaussian mixture models) Optional: Reynolds, Gaussian Mixture Models tutorial	Clustering and mixture models
Thu, Nov 26, 2026	Thanksgiving Break — no class

Theme 6 · Neural Networks, Synthesis, and Review

Date	Topic	Reading	Assignments
Tue, Dec 1, 2026	Missing data and neural-network foundations	Missing-data paper Stanford CS231n, Neural Networks Part 1	Neural networks
Thu, Dec 3, 2026	Deep learning and applications	Optional: Karpathy, Neural Networks: Zero to Hero
Tue, Dec 8, 2026	Data science synthesis and applications
Thu, Dec 10, 2026	Course wrap-up and final-exam review