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	Lecture 7 Evaluation and an introduction to probability [slides] [handout]	MML, §§6.1–6.3 Daumé, §§9.1–9.4	HW04: Evaluation metrics Due: 2026-10-02
Thu, Sep 24, 2026	Lecture 8 Naive Bayes, conditional independence, and probabilistic classification [slides] [handout]	Daumé, §§9.1–9.4
Tue, Sep 29, 2026	Lecture 9 Naive Bayes, Smoothing Decision Trees [slides] [handout]	Speech and Language Processing (3rd ed. draft) by Jurafsky & Martin: Appendix B: B1-B6	HW05: Naive Bayes Due: 2026-10-27
Thu, Oct 1, 2026	Lecture 10 Information, entropy, coding theory, and applications in machine learning [slides] [handout]	MIT notes, Information, Entropy, and the Motivation for Source Codes Understanding Entropy
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	Logistic regression
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	EMNLP Conference - no class	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