📔 Online Prediction and Learning

📌 Logistics: Lecture notes consists of materials from my coursework at IISc.

• Lec-1: Introduction to Online Learning.
• Lec-2: Predictions with Experts
• Lec-3: Halving Algorithm
• Lec-4: Weighted Majority (WMAJ) & Random-WMAJ Algorithms
• Lec-5: Convex Analysis and Online Convex Learning
• Lec-6: Follow The Leader (FTL)
• Lec-7: Introduction to Follow the Regularized Leader (FTRL)
• Lec-8: Analysis of FTRL
• Lec-9: FTRL futher examples and analysis
• Lec-10: Geometrix view of FTRL
• Lec-11: Convex Geometry & Dual Functions
• Lec-12: Frenchel Duality
• Lec-13: Online Mirror Descent
• Lec-14: Optimization Theory view
• Lec-15: Online Convex Optimization with Bandits
• Lec-16: Online Linear Optimization with Bandits Feedback
• Lec-17: Stochastic Bandit Algorithms
• Lec-18: Exploration vs. Exploitation
• Lec-19: Hoeffding’s Inequality and Intro to UCB
• Lec-20: UCB: Regret bound analysis
• Lec-21: Introduction to Thompson Sampling
• Lec-22 & Lec-23: Regret Analysis: Thompson Sampling
• Lec-24: General Stochastic Multiarm Bandits
• Lec-25 & Lec-26 & Lec-27: Fundamental limits of Bandit learning
• Lec-28: Structured Bandits