ECE365: Fundamentals of Machine Learning (Lectures)

You can find the typed notes for this class here. They will be updated as needed (with a changelog below). The course follows essentially linearly with the notes.

All pre-lecture notes are given. Post-lecture notes will be given after each class session.

Please finish the corresponding pre-lecture notes before coming to class.

	Date	Content	Pre-Lecture	Post-Lecture	Class Recording
Lecture 1	Aug 25th	Introduction to the course; Review of linear algebra and probability	[link]	[link]	[link]
Lecture 2	Aug 27th	k-Nearest Neighbor Classifiers and Bayes Classifiers	[link]	[link]	[link]
Lecture 3	Sep 1st	Linear Classifiers, Linear Discriminant Analysis, and Logistic Regression	[link]	[link]	[link]
Lecture 4	Sep 3rd	Support Vector Machines, Naive Bayes Classifer	[link]	[link]	[link]
Lecture 5	Sep 8th	Kernel Trick, How to Handle Data	[link]	[link]	[link]
Lecture 6	Sep 10th	K-means Clustering	[link]	[link]	[link]
Lecture 7	Sep 15th	Linear Regression	[link]	[link]	[link]
Lecture 8	Sep 17th	Eigen-Decomposition	[link]	[link]	[link]
Lecture 9	Sep 22nd	SVD	[link]	[link]	[link]
Lecture 10	Sep 24th	PCA and wrap up	[link]	[link]	[link]