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 | Data representation, imputation, scaling, categorical encoding, transformers, pipelines, and preprocessing within cross-validation. |
Lecture 6: sklearn column transformer and text fearutres
|
Column transformer, arguments of OHE, encoding text features, incorporating text features in an ML pipeline |
| Lecture 7: Linear models |
Intuition behind linear models, linear regression and logistic regression, scikit-learn’s Ridge model, prediction probabilities, interpret model predictions using coefficients learned by a linear model, parametric vs. non-parametric models
|
| Lecture 8: Hyperparameter Optimization | motivation for hyperparameter optimization, hyperparameter optimization using sklearn’s GridSearchCV and RandomizedSearchCV, optimization bias |
| Lecture 9: Classification metrics | confusion metrics, precision, recall, f1-score, PR curves, AP score, ROC curve, ROC AUC, class imbalance |
| Lecture 10: Regression metrics | Classification metrics revision, ridge and alpha, MAE, MSE, RMSE, R2, MAPE, cross-validation scoring, target transformations |
| Lecture 11: Ensembles | |
| Lecture 12: Feature importances | |
| Lecture 13: Feature engineering and selection | Feature engineering, interaction features, numeric transformations, model-based selection, RFE |
| Lecture 14: K-Means | Unsupervised learning, K-Means, representation, choosing k |
| Lecture 15: DBSCAN and hierarchical clustering | K-Means limitations, DBSCAN, hierarchical clustering, dendrograms, linkage, and interpreting clusters |
| Lecture 16: Recommender systems | Recommender systems and content-based regression |
| Lecture 17: Introduction to Computer Vision | Computer vision and transfer learning |
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