ECE398BD: 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 schedules of each lecture are given below.
Lecture 1 |
Introduction to the course; Review of linear algebra and probability |
Lecture 2 |
Elements of Machine Learning |
Lecture 3 |
k-Nearest Neighbor Classifier and Bayes Classifier |
Lecture 4 |
Preview of Parts 2 and 3 (Do and Bose) |
Lecture 5 |
Linear Classifiers and Linear Discriminant Analysis |
Lecture 6 |
Kernel Tricks and Support Vector Machines |
Lecture 7 |
Linear Regression |
Lecture 8 |
K-means Clustering |
Lecture 9 |
SVD and Eigen-Decomposition |
Lecture 10 |
PCA and Applications |
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