📆 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	Recursive Estimation
14	Kalman Filter	HW7	Understanding KF
15	Kalman Filter
16	Kalman Filter	HW8
17	GR1 (L1-L17)
18	Project 1
19
20		Proj1
21	Statistical Inference
22	MLE
23	Linear Regression	HW9
24	Linear Regression
25	Gradient Descent
26	Logistic Regression
27	Naive Bayes	HW10
28	Assessment & Validation
29	MAP
30	Regularization	HW11
31	SVM
32	Snow-Day	HW12
33	Neural Networks
34	Back propagation	HW13
35	GR2
36	Final Project	HW14
37	Final Project
	Thanksgiving Break
38	Final Project
39	Final Project
40	Final Project	Final Report