📆 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	Least Squares Estimation	HW5
12	Optimal Estimation
13	Recursive Estimation
14	Kalman Filter	HW6	Understanding KF
15	Kalman Filter
16	Project 1	HW8
17	Project 1
18	GR1 (L1-L15 & HW1-HW7)
19	Information Theory	Proj1
20	Mathematical Optimization
21	Maximum Likelihood Estimate	HW9
22	Maximum Likelihood Estimate
23	Maximum a Priori	HW10
24	Linear Regression
25	Gradient Descent	HW11
26	Special Topic
27	Logistic Regression	HW12
28	Naive Bayes
29	Assessment & Validation	HW13
30	Regularization
31	SVM	HW14
32	Neural Networks
33	Back propagation
34	Back propagation	HW15
35	GR2
36	Final Project
37	Final Project
	Thanksgiving Break
38	Final Project
39	Final Project
40	Final Project
41	Final Project	Final Report