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 | [link] |
Lecture 2 | k-Nearest Neighbor Classifiers and Bayes Classifiers | [link] |
Lecture 3 | Linear Classifiers and Linear Discriminant Analysis | [link] |
Lecture 4 | Naive Bayes and Kernel Tricks | [link] |
Lecture 5 | Logistic Regression, Support Vector Machines and Model Selection | [link] |
Lecture 6 | K-means Clustering | [link] |
Lecture 7 | Linear Regression | [link] |
Lecture 8 | Eigen-Decomposition | [link] |
Lecture 9 | SVD | [link] |
Lecture 10 | PCA and wrap up | [link] |
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