(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 | slides |
notes (SVM) notes (LagrangianDual) slides (lab) code (iris classification) code (decision boundary visualization) code (A2 starter code) |
| Decision trees & random forest | slides |
notes (decision trees & random forest) notes (data-features-evaluation) slides (lab) code (underfitting and overfitting) |
| Neural networks | slides |
notes (neural networks) notes (backpropagation) code (neural networks) |
| Convolutional neural networks | slides |
notes (CNN) slides (lab) code (PyTorch and deep learning) |
| Guest lecture Feedback on A2 Info final exam Q&A |
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