📆 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)

Lesson	Topic	Due	Reading
1	Course Intro
2	Intro to Machine Learning	Cadet Intro	S:1.1-1.6
3	Linear Algebra	HW1	S:2.5-2.6,M:7.1
4	Basis		3Blue1Brown
5	Eigenvalues & Eigenvectors	HW2	3Blue1Brown
6	Eigenvalues & Eigenvectors
7	Least Squares Estimation	HW3	B:1.1
8	Least Squares Estimation		B:1.1
9	Special Topic	HW4
10	Lab1: Python & LSE
11	Joint Distributions	HW5	S:2.1-2.4,2.7-2.8
12	Multivariate Gaussian	Lab1	S:2.9-2.10,M:2.1-2.2,B: 1.2
13	Optimal Estimation	HW6
14	Recursive Estimation
15	Recursive Estimation
16	Kalman Filter	HW7	Understanding KF
17	Kalman Filter
18	Kalman Filter	HW8
19	GR1 (L1-L17)
20	Project 1
21	Statistical Inference		S:3.1-3.9, B:3.2-3.3
22	MLE	Proj1	M:4.2
23	Linear Regression	HW9	S:4.1-4.3
24	Linear Regression		M:11.2
25	Gradient Descent		S:5.8-5.10
26	Logistic Regression		S:6.3, B:4.3
27	Naive Bayes	HW10	M:9.3
28	Assessment & Validation		S:4.4-4.6
29	MAP		S:4.7-4.11
30	Regularization	HW11	S:4.7-4.11
31	SVM
32	Snow-Day	HW12	S:5.14-5.15
33	Neural Networks		S:6.4
34	Back propagation	HW13	S:5.14-5.15
35	GR2
36	Final Project	HW14
37	Final Project
	Thanksgiving Break
38	Final Project
39	Final Project
40	Final Project	Final Report