Lecture notes
A guide to the course notes. On this page I will post the course notes that will be used during each lecture. These will be slides, and will be posted before the lecture. After each lecture, the annotated slides will be posted too. One "Lecture" file may be used for more than one actual lecture. To eliminate confusion, the unannotated notes posted here will be titled Lecture I, Lecture II, etc, while the annotated notes after each lecture, will be titled L1a-jan6, L1p-jan6, L2a-jan8, etc.
Lecture templates
- Lecture 0 Overview of Machine Learnng
- Lecture I Prediction: examples. The Nearest Neighbor (NN) predictor.
Lecture I-1 Bias and Variance for the Nearest Neighbor (NN) predictor
- Lecture II Linear regression and classification. Loss Functions. Maximum likelihood. UPDATED 2/2
- Lecture III CART
- Lecture IV Neural Networks -- Part 1 (backprop to be added)
Annotated lecture slides
- L1a-jan6, L1p-jan6 What is ML?
- L2a-jan8, L2p-jan8 Predictors by type of output. Nearest neighbor predictor.
- L3a-jan13, L3p-jan13 K-NN bias-variance tradeoff
- L4a-jan15, L4p-jan15 Losses. Linear regression by LS. (1/20/2026)
- L5a-jan22, L5p-jan22 Linear regression by ML
- L6a-jan27, L6p-jan27 Perceptron, LDA, Logistic regression
- L7a-jan29, L7p-jan29
(--a = 11:30-12:60 section, --p =4-6:20 section)
Refresher materials
- Calculus and linear algebra by Haochen Sun here
- Probability and Statistics by Gavin Deane here. Sample tutorial problem solutions: Q1, Q2(a), Q2(b), Q3.
- Plotting data with matplotlib by Henry Lin here. Data here
|