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.

The links to each lecture's in class (handwritten) notes are given below.

Lecture 1 Introduction to the course; Review of linear algebra and probability [notes]
Lecture 2 k-Nearest Neighbor Classifiers and Bayes Classifiers [notes]
Lecture 3 Linear Classifiers and Linear Discriminant Analysis [notes]
Lecture 4 Naive Bayes and Kernel Tricks [notes]
Lecture 5 Logistic Regression, Support Vector Machines and Model Selection [notes]
Lecture 6 K-means Clustering [notes]
Lecture 7 Linear Regression [notes]
Lecture 8 Eigen-Decomposition [notes]
Lecture 9 SVD [notes]
Lecture 10 PCA and wrap up [notes]