|
ECE398BD: Fundamentals of Machine Learning (Lectures)
Some lecture notes will be emailed to you.
| 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 | Kernel Tricks and Support Vector Machines |
| Lecture 5 | Model Selection & Assessment and K-means clustering |
| Lecture 6 | K-means Clustering (cont.) and Linear Regression |
| Lecture 7 | SVD and Eigen-Decomposition |
| Lecture 8 | SVD and Eigen-Decomposition (cont.) |
| Lecture 9 | PCA and Applications, Q&A
|
|