(materials will be available after each lecture. Course calendar)
| Lectures (grouped by topics) |
Links | |||
| Introduction to machine learning | BK-IZ U | slides | notes | |
| Clustering & nearest neighbor classification | BK-CZ D | slides | notes | |
| Linear regression & gradient descent | BK-CZ D | slides |
notes code (GradientDescent) code (Optimization - 1) code (Optimization - 2) |
|
| Bayesian classification & logistic regression | BK-CZ D | slides |
notes (BayesianClassification) notes (LogisticRegression) |
|
| Support vector machine | BK-CZ D | slides |
notes (SVM) notes (Metrics) code (Standard SVM) code (Kernels) |
|
| Decision trees & random forest | BK-CZ D | slides |
notes code (Classification) |
|
| Neural networks | BK-CZ D | slides |
notes (NeuralNetworks) notes (Backpropagation) code |
|
| Deep learning | BK-CZ D | slides |
notes (CNN) code (CNN) |
|
| Summary; Q&A | BK-CZ U | about final exam | ||
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