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.
Lecture 1 |
Introduction to the course; Review of linear algebra and probability |
Lecture 2 |
k-Nearest Neighbor Classifiers and Bayes Classifiers |
Lecture 3 |
Linear Classifiers and Linear Discriminant Analysis |
Lecture 4 |
Naive Bayes and Kernel Tricks |
Lecture 5 |
Logistic Regression, Support Vector Machines and Model Selection |
Lecture 6 |
K-means Clustering |
Lecture 7 |
Linear Regression |
Lecture 8 |
SVD and Eigen-Decomposition |
Lecture 9 |
Principal Component Analysis |
Lecture 10 |
Optimization Methods for Machine Learning, Q&A |
|