📆 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-L16 & HW1-HW8)
18	Product Rule
19	Bayes & Conditional Independence	Proj1
20	Maximum Likelihood Estimate
21	Maximum Likelihood Estimate
22	Special Topic (Col Trimble)	HW9
23	Maximum a Priori
24	Linear Regression	HW10
25	Linear Regression
26	Gradient Descent	HW11
27	Logistic Regression
28	Naive Bayes	HW12
29	Assessment & Validation
30	Regularization	HW13
31	SVM
32	Neural Networks	HW14
33	Back propagation
34	Final Project	HW15
35	GR2
36	Final Project
37	Final Project
	Thanksgiving Break
38	Final Project
39	Final Project
40	Final Project	Final Report