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]