📆 Course Schedule

Note

This schedule is subject to change as appropriate.

Last Updated: 30 Jun 2024

Reading:

• S: Simeone, Machine Learning for Engineers (Required)

• 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	Linear Algebra	HW1
4	Basis	HW2	3Blue1Brown
5	Eigenvalues & Eigenvectors		3Blue1Brown
6	Least Squares Estimation	HW3
7	Least Squares Estimation
8	Lab1: Python & LSE	HW4
9	Joint Distributions
10	Multivariate Gaussian	HW5
11	Optimal Estimation
12	Recursive Estimation	HW6
13	Kalman Filter		Understanding KF
14	Kalman Filter	HW7
15	Project 1
16	Project 1	HW8
17	GR1 (L1-L17)
18	Conditional Independence
19	MLE	Proj1
20	MAP	HW9
21	Linear Regression
22	Linear Regression	HW10
23	Gradient Descent
24	Logistic Regression	HW11
25	Naive Bayes
26	Assessment & Validation	HW12
27	Regularization
28	SVM	HW13
29	Neural Networks
30	Back propagation	HW14
31	Back propagation
32	Project 2
33	Project 2
34
35	GR2
36	Final Project
37	Final Project
	Thanksgiving Break
38	Final Project
39	Final Project
40	Final Project	Final Report