NOC:Bandit Algorithm (Online Machine Learning)

Lecture 1 - Introduction to Online Learning - I

Lecture 2 - Introduction to Online Learning - II

Lecture 3 - Basics of Statistical Learning

Lecture 4 - Empirical risk minimization

Lecture 5 - Consistency Halving algorithm

Lecture 6 - Online Learnability

Lecture 7 - Standard Optimal Algorithm

Lecture 8 - Classification in unrealizability case

Lecture 9 - Covers Impossibility Result

Lecture 10 - Weighted Majority

Lecture 11 - Proof Weighted Majority

Lecture 12 - Full Information vs Bandit Setting

Lecture 13 - Adversarial Bandit Setting

Lecture 14 - Exponential Weights for Exploration and Exploitation Algorithm

Lecture 15 - Regret Bound of Exp3

Lecture 16 - Regret Bound of Exp3 (Continued...)

Lecture 17 - Exp3.P and Exp3.IX

Lecture 18 - Online Convex Optimisation

Lecture 19 - Follow the Leader (FTL) Algorithm

Lecture 20 - Follow the Regularized Leader

Lecture 21 - Online Gradient Descent

Lecture 22 - Strongly Convex Function

Lecture 23 - FoReL with Strongly Convex Regulariser

Lecture 24 - FoReL with Strongly Convex Regulariser (Continued...)

Lecture 25 - Euclidean and Entropy Regularizer

Lecture 26 - Introduction to Stochastic Bandits

Lecture 27 - Concentration Inequalities

Lecture 28 - Subgaussian Random Variable

Lecture 29 - Regret Definition and Regret Decomposition

Lecture 30 - Explore and Commit (ETC) Algorithm

Lecture 31 - Regret Analysis and ETC

Lecture 32 - Optimism in the Face of Uncertainty

Lecture 33 - Upper Confidence Bound Algorithm

Lecture 34 - Regret Analysis of UCB

Lecture 35 - Problem Dependent and Independent Bounds of UCB

Lecture 36 - KL-UCB Algorithm

Lecture 37 - Thompson Sampling - Brief Discussion

Lecture 38 - Proof Idea of Lower Bounds - 1

Lecture 39 - Proof Idea of Lower Bounds - 2

Lecture 40 - Proof of Lower Bound - 1

Lecture 41 - Proof of Lower Bound - 2

Lecture 42 - Stochastic Contextual Bandits

Lecture 43 - Introduction to Stochastic Linear Bandits

Lecture 44 - Stochastic Linear Bandits

Lecture 45 - Regret Analysis of SLB - I

Lecture 46 - Regret Analysis of SLB - II

Lecture 47 - Regret Analysis of SLB - III

Lecture 48 - Construction of Confidence Ellipsoids - I

Lecture 49 - Construction of Confidence Ellipsoids - II

Lecture 50 - Adversarial Contextual Bandits - I

Lecture 51 - Adversarial Contextual Bandits - II

Lecture 52 - Exp4 Algorithm

Lecture 53 - Regret of Exp4

Lecture 54 - Adversarial Linear Bandits

Lecture 55 - Exp3 for Adversarial Linear Bandits

Lecture 56 - Introduction to Pure Exploration and its lower bounds

Lecture 57 - Uniform Exploration

Lecture 58 - KL-LUCB

Lecture 59 - Lil’ UCB

Lecture 60 - Lower Bound for Pure Exploration Problem