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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 |
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