📆 Course Schedule

Note

This schedule is subject to change as appropriate.

Last Updated: 16 Jul 2026

Reading:

• Jiang, Machine Learning Fundamentals (chapter/section numbers refer to this text)

Note

This schedule follows Hui Jiang’s Machine Learning Fundamentals in its own chapter/section order (Ch1 → Ch2.1-2.4 → Ch4 → Ch6 → Ch7 → Ch8 → Ch9 → Ch10), with Ch10 (Bayesian Decision Theory, MAP, MLE) taught last. We still do not cover the entire book — Ch5 (Statistical Learning Theory) and most of Ch11-15 are skipped and left as optional reading, except the Naive Bayes case study pulled from Ch15.2.5.

Lab1 (Python & LSE) is now dropped entirely, along with the Least Squares Estimation lectures dropped earlier — this course no longer has a dedicated least-squares/Python lab; cadets first hit least-squares fitting in the Linear Regression lesson (Ch6.2) and get their Python practice through HW and the Final Project. Removing Lab1 shifted GR1 to L14, one lesson earlier than the previously requested L15-L18 window — flag if you’d like it nudged back into that range (e.g. by re-splitting one more lesson elsewhere).

Dropping Lab1 freed a slot, which went to un-combining “Learning Algorithms for Neural Networks” back into its two components — Loss & Automatic Differentiation (Ch8.3.1-8.3.2) and SGD & Backpropagation (Ch8.3.3) — since Ch8.3.2 (automatic differentiation) alone is a dense 12-page section. Everything from L27 (Heuristics and Tricks for Optimization) onward is unchanged from the previous revision, including GR2 at L35 and the Final Project fixed at 6 lessons (L36-41) with Thanksgiving Break between L37 and L38.

Pacing is calibrated against this course’s own historical slide decks, corrected for the fact that each deck pairs a blank fill-in-the-blank slide with a filled-in version, plus an opening slide and an announcement slide that carry no new material. Counting only actual content slides (raw slide count / 2, minus 2), the historical median and mean are both 14 content slides per 50-minute lesson across 28 prior lectures.

Lsn	Topic	Due	Reading
1	Course Intro
2	Intro to Machine Learning	Cadet Intro	Ch1 Introduction
3	Vectors & Matrix Basics		Ch2.1 Linear Algebra
4	Linear Maps & Determinants
5	Basis	HW1	3Blue1Brown
6	Eigenvalues & Eigenvectors		3Blue1Brown
7	Probability and Statistics	HW2	Ch2.2
8	Probability and Statistics: Distributions & Transformations		Ch2.2
9	Information Theory	HW3	Ch2.3
10	Mathematical Optimization		Ch2.4
11	Feature Extraction: PCA & LDA	HW4	Ch4.1-4.2
12	Feature Extraction: Nonlinear Methods	HW5	Ch4.3-4.4
13	GR1 Review
14	GR1 (L1-L12 & HW1-HW5)
15	Perceptron		Ch6.1
16	Linear Regression		Ch6.2
17	Minimum Classification Error	HW6	Ch6.3
18	Logistic Regression		Ch6.4
19	Support Vector Machines	HW7	Ch6.5
20	Support Vector Machines (Kernel & Soft Margin)		Ch6.5
21	Learning Discriminative Models: Regularization	HW8	Ch7.1-7.2
22	Neural Networks: Architecture		Ch8.1-8.2.1
23	Neural Network Structures: CNNs & RNNs	HW9	Ch8.2.2-8.2.4
24	Neural Network Structures: Transformer		Ch8.2.5
25	Learning Algorithms: Loss & Automatic Differentiation		Ch8.3.1-8.3.2
26	Learning Algorithms: SGD & Backpropagation	HW10	Ch8.3.3
27	Heuristics and Tricks for Optimization		Ch8.4-8.5
28	Ensemble Learning: Decision Trees	HW11	Ch9.1
29	Ensemble Learning: Bagging & Boosting		Ch9.2-9.3
30	Overview of Generative Models: Bayesian Decision Theory	HW12	Ch10.1-10.2
31	Statistical Data Modeling: Plug-In MAP Decision Rule		Ch10.3
32	Density Estimation: Maximum-Likelihood Estimation	HW13	Ch10.4
33	Case Study: Naive Bayes Classifier		Ch15.2.5
34	GR2 Review
35	GR2
36	Final Project
37	Final Project
	Thanksgiving Break
38	Final Project
39	Final Project
40	Final Project
41	Final Project	Final Report