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