Unit 1 · Supervised Learning

# Syllabus

• Basic methods: Distance-based methods, Nearest-Neighbours, Decision Trees, Naive Bayes. Linear models: Linear Regression, Logistic Regression, Generalized Linear Models. Support Vector Machines, Nonlinearity and Kernel Methods. Beyond Binary Classification: Multi-class/Structured Outputs, Ranking

# Topics

• 1.1Supervised Learning: Classification vs Regression★★
• 1.2Distance-Based Methods & Distance Measures★★
• 1.3K-Nearest Neighbours (KNN)★★★
• 1.4Decision Trees: ID3, Entropy & Information Gain★★
• 1.5Probability Basics & Bayes Theorem★
• 1.6Bayesian Learning & Inference☆
• 1.7Naive Bayes Classifier★★
• 1.8Linear Regression★★★
• 1.9Logistic Regression★★★
• 1.10Generalized Linear Models☆
• 1.11Support Vector Machines: Hyperplane, Margin & Support Vectors★
• 1.12Non-linearity & Kernel Methods (Non-linear SVM)★
• 1.13Beyond Binary Classification: Multi-class, Structured Outputs & Ranking☆