Supervised Learning in Machine Learning

TEACHING UNIT 1. FUNDAMENTALS OF SUPERVISED LEARNING 1. Definition of supervised learning 2. Differences from other paradigms: unsupervised and reinforcement learning 3. Types of problems: classification vs. regression 4. Typical supervised ML pipeline 5. Model evaluation: Key metrics and cross-validation TEACHING UNIT 2. DATA PREPARATION AND FEATURE ENGINEERING 1. Data quality: cleaning, handling missing values and outliers 2. Encoding categorical variables 3. Scaling and normalizing features 4. Selection and extraction of relevant features 5. Dimension reduction techniques (PCA, LDA) TEACHING UNIT 3. BASIC SUPERVISED LEARNING MODELS 1. Linear regression and its variants 2. Linear classifiers: perceptron and logistic regression 3. Decision trees: structure, pruning, and splitting criteria 4. K-Nearest Neighbors (k-NN): simplicity and limitations 5. Model-specific evaluation metrics TEACHING UNIT 4. ADVANCED MODELS AND ENSEMBLE METHODS 1. Support Vector Machines (SVM): margins and kernels 2. Bagging, Boosting, and Stacking 3. Random Forest and its advantages over individual trees 4. Gradient Boosting Machines 5. Hyperparameter tuning and nested validation