ECE365: Fundamentals of Machine Learning (Lectures)

You can find the typed notes from the last semester here.

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

Date Content Pre-Lecture Post-Lecture Class Recording
Lecture 1 Jan 26th Introduction to the course; Review of linear algebra and probability [link] [link] [link]
Lecture 2 Jan 28th k-Nearest Neighbor Classifiers and Bayes Classifiers [link] [link] [link]
Lecture 3 Feb 2nd Linear Discriminant Analysis (LDA), Linear Classifiers, Discriminant Functions [link] [link] [link]
Lecture 4 Feb 4th Logistic Regression, Support Vector Machines, Naive Bayes Classifer [link] [link] [link]
Lecture 5 Feb 9th Kernel Trick, How to Handle Data [link] [link] [link]
Lecture 6 Feb 11th K-means Clustering [link] [link] [link]
Lecture 7 Feb 16th Linear Regression [link] [link] [link]
Lecture 8 Feb 18th Eigen-Decomposition [link] [link] [link]
Lecture 9 Feb 23rd SVD [link] [link] [link]
Lecture 10 Feb 25th PCA and wrap up [link] [link] [link]