📆 Course Schedule

Note

This schedule is subject to change as appropriate.

Last Updated: 30 Jun 2026

Reading:

• J: Jiang, Machine Learning Fundamentals

• M: Murphy, Probabilistic Machine Learning (Optional)

• B: Biship, Pattern Recognition and Machine Learning (Optional)

Lsn	Topic	Due	Reading
1	Course Intro
2	Intro to Machine Learning	Cadet Intro
3	Vectors & Matrix Basics	HW1
4	Linear Maps & Determinants
5	Basis	HW2	3Blue1Brown
6	Eigenvalues & Eigenvectors		3Blue1Brown
7	Random Variables	HW3
8	Multivariate Gaussian
9	Least Squares Estimation	HW4
10	Least Squares Estimation
11	Lab1: Python & LSE	HW5
12	Optimal Estimation
13	Recursive Estimation	HW6
14	Kalman Filter		Understanding KF
15	Kalman Filter	HW7
16	Special Topic
17	Project 1
18	Project 1	HW8
19	GR1 (L1-L16 & HW1-HW8)
20	Information Theory	Proj1
21	Mathematical Optimization
22	Maximum Likelihood Estimate
23	Maximum Likelihood Estimate
24	Maximum a Priori
25	Linear Regression	HW10
26	Special Topic	HW11
27	Gradient Descent
28	Logistic Regression	HW11
29	Naive Bayes
30	Assessment & Validation	HW12
31	Regularization
32	SVM	HW13
33	Neural Networks
34	Back propagation	HW14
35	Back propagation	HW14
36	GR2
37	Final Project
	Thanksgiving Break
38	Final Project
39	Final Project
40	Final Project
41	Final Project	Final Report