(materials will be available after each lecture. Course calendar)
| Lectures (grouped by topics) |
Materials | |
| Introduction to machine learning | slides | notes (introduction) |
| Linear regression & gradient descent | slides |
notes (linear regression) code (gradient descent) |
| Clustering & nearest neighbor classification | slides |
notes (clustering) notes (nearest neighbor classification) |
| Bayesian classification & logistic regression | slides |
notes (Bayesian classification) notes (logistic regression) |
| Support vector machine |
notes (SVM) code (iris classification) code (decision boundary visualization) code (A2 starter code) |
|
| Decision trees & random forest | ||
| Neural networks | ||
| Convolutional neural networks | ||
| Guest lecture Info project presentation Q&A |
||
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