Overview
Schedule
| Title | Description |
|---|---|
| Lecture 1: Course introduction | What is machine learning, types of machine learning, learning to navigate through the course materials, getting familiar with the course policies |
| Lecture 2: Terminology, baselines, decision Trees | Supervised machine learning terminology: Features, target, examples, training, parameters and hyperparameters, Decision boundary, classification vs. regression, inference vs. prediction, accuracy vs. error, baselines, intuition of decision trees |
| Lecture 3: ML fundamentals | Generalization, data splitting, cross-validation, overfitting, underfitting, the fundamental tradeoff, the golden rule |
| Lecture 4: \(k\)-nearest neighbours and SVM RBFs |
Introduction to KNNs, hyperparameter n_neighbours or \(k\), C and gamma hyperparameters of SVM RBF, decision boundaries with different values of hyperparameters.
|
| Lecture 5: Preprocessing and sklearn pipelines |
Preprocessing motivation, Common transformations in sklearn, sklearn transformers vs. Estimators, The golden rule in the feature transformations, sklearn pipelines
|
Lecture 6: sklearn column transformer and text fearutres
|
Column transformer, arguments of OHE, encoding text features, incorporating text features in an ML pipeline |
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