(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) |
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 (from lecture notes) code (decision boundary visualization) |
Decision trees & random forest | slides |
notes (decision trees & random forest) notes (performance metrics) slides (lab) code (naive classification) 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) |
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