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 |
| 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 recap, ridge and RidgeCV, alpha hyperparameter of ridge, MSE, RMSE, MAPE, log transformations on the target
|
| Lecture 11: Midterm review | |
| Lecture 12: Ensembles | |
| Lecture 13: Feature importances | |
| Lecture 14: Feature engineering and selection | Motivation for feature engineering, preliminary feature engineering on text data, general concept of feature selection, model-based feature selection vs. RFE |
| Lecture 15: K Means | Unsupervised paradigm, motivation and potential applications of clustering, K-Means algorithm, pros and cons of K-Means, the Elbow plot and Silhouette plots for a given dataset, importance of input data representation in clustering. |
| Lecture 16: DBSCAN and hierarchical | Limitations of K-Means, DBSCAN, ierarchical clustering, dendrograms, omparing and contrasting different clustering methods |
| Lecture 17: Recommender systems | |
| Lecture 18: Natural Language Processing | |
| Lecture 19: Introduction to computer vision | |
| Lecture 20: Time series | |
| Lecture 21: Survival analysis | |
| Lecture 22: Communication | |
| Lecture 24: Deployment and conclusion |
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