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
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