ECE 365: Fundamentals of Machine Learning (Lectures)

You can find the typed notes here.

Corresponding Pre-lecture course notes will be given before each lecture. Post-lecture notes will be given after each class session.

Gradescope entry code is here. Please change your gradescope Student ID to your University UIN.

	Date	Content	Pre-Lecture	Post-Lecture
Lecture 1	Aug 23th	Introduction to the course; Review of linear algebra and probability	[link]	[link]
Lecture 2	Aug 25th	k-Nearest Neighbor Classifiers and Bayes Classifiers	[link]	[link]
Lecture 3	Aug 30th	Linear Classifiers and Linear Discriminant Analysis	[link]	[link]
Lecture 4	Sep 1st	Naive Bayes Classifer, Kernel Trick	[link]	[link]
Lecture 5	Sep 6th	Logistic Regression, SVM, and Model Selection	[link]	[link]
Lecture 6	Sep 8th	K-means Clustering and Applications	[link]	[link]
Lecture 7	Sep 13th	Linear Regression and Applications	[link]	[link]
Lecture 8	Sep 15th	SVD and Eigen-Decomposition	[link]	[link]
Lecture 9	Sep 20th	Principal Component Analysis	[link]	[link]
Lecture 10	Sep 22nd	Introduction to Neural Networks	[link]	[link]