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(materials will be available after each lecture. Course calendar)

(grouped by topics)
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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