| Module Name | Download |
|---|---|
| noc20_mg37_assignment_Week_1 | noc20_mg37_assignment_Week_1 |
| noc20_mg37_assignment_Week_10 | noc20_mg37_assignment_Week_10 |
| noc20_mg37_assignment_Week_11 | noc20_mg37_assignment_Week_11 |
| noc20_mg37_assignment_Week_12 | noc20_mg37_assignment_Week_12 |
| noc20_mg37_assignment_Week_2 | noc20_mg37_assignment_Week_2 |
| noc20_mg37_assignment_Week_3 | noc20_mg37_assignment_Week_3 |
| noc20_mg37_assignment_Week_4 | noc20_mg37_assignment_Week_4 |
| noc20_mg37_assignment_Week_5 | noc20_mg37_assignment_Week_5 |
| noc20_mg37_assignment_Week_6 | noc20_mg37_assignment_Week_6 |
| noc20_mg37_assignment_Week_7 | noc20_mg37_assignment_Week_7 |
| noc20_mg37_assignment_Week_8 | noc20_mg37_assignment_Week_8 |
| noc20_mg37_assignment_Week_9 | noc20_mg37_assignment_Week_9 |
| Sl.No | Chapter Name | MP4 Download |
|---|---|---|
| 1 | Lecture 1: Introduction to Online Learning –I | Download |
| 2 | Lecture 2: Introduction to Online Learning –I | Download |
| 3 | Lecture 3: Basics of Statistical Learning | Download |
| 4 | Lecture 4: Empirical risk minimization | Download |
| 5 | Lecture 5: Consistency Halving algorithm | Download |
| 6 | Lecture 6: Online Learnability | Download |
| 7 | Lecture 7: Standard Optimal Algorithm | Download |
| 8 | Lecture 8: Classification in unrealizability case | Download |
| 9 | Lecture 9: Covers Impossibility Result | Download |
| 10 | Lecture 10: Weighted Majority | Download |
| 11 | Lecture 11: Proof Weighted Majority | Download |
| 12 | Lecture 12: Full Information vs Bandit Setting | Download |
| 13 | Lecture 13: Adversarial Bandit Setting | Download |
| 14 | Lecture 14: Exponential Weights for Exploration and Exploitation Algorithm | Download |
| 15 | Lecture 15: Regret Bound of Exp3 | Download |
| 16 | Lecture 16: Regret Bound of Exp3(Contd.) | Download |
| 17 | Lecture 17: Exp3.P and Exp3.IX | Download |
| 18 | Lecture 18: Online Convex Optimisation | Download |
| 19 | Lecture 19: Follow the Leader (FTL) Algorithm | Download |
| 20 | Lecture 20: Follow the Regularized Leader | Download |
| 21 | Lecture 21: Online Gradient Descent | Download |
| 22 | Lecture 22: Strongly Convex Function | Download |
| 23 | Lecture 23: FoReL with Strongly Convex Regulariser | Download |
| 24 | Lecture 24: FoReL with Strongly Convex Regulariser (Contd.) | Download |
| 25 | Lecture 25: Euclidean and Entropy Regularizer | Download |
| 26 | Lecture 26: Introduction to Stochastic Bandits | Download |
| 27 | Lecture 27: Concentration Inequalities | Download |
| 28 | Lecture 28: Subgaussian Random Variable | Download |
| 29 | Lecture 29: Regret Definition and Regret Decomposition | Download |
| 30 | Lecture 30: Explore and Commit (ETC) Algorithm | Download |
| 31 | Lecture 31: Regret Analysis and ETC | Download |
| 32 | Lecture 32: Optimism in the Face of Uncertainty | Download |
| 33 | Lecture 33: Upper Confidence Bound Algorithm | Download |
| 34 | Lecture 34 : Regret Analysis of UCB | Download |
| 35 | Lecture 35 : Problem Dependent and Independent Bounds of UCB | Download |
| 36 | Lecture 36 : KL-UCB Algorithm | Download |
| 37 | Lecture 37 : Thompson Sampling - Brief Discussion | Download |
| 38 | Lecture 38 : Proof Idea of Lower Bounds - 1 | Download |
| 39 | Lecture 39 : Proof Idea of Lower Bounds - 2 | Download |
| 40 | Lecture 40 : Proof of Lower Bound-1 | Download |
| 41 | Lecture 41 : Proof of Lower Bound-2 | Download |
| 42 | Lecture 42 : Stochastic Contextual Bandits | Download |
| 43 | Lecture 43 : Introduction to Stochastic Linear Bandits | Download |
| 44 | Lecture 44 : Stochastic Linear Bandits | Download |
| 45 | Lecture 45 : Regret Analysis of SLB-I | Download |
| 46 | Lecture 46 : Regret Analysis of SLB - II | Download |
| 47 | Lecture 47 : Regret Analysis of SLB-III | Download |
| 48 | Lecture 48 : Construction of Confidence Ellipsoid - I | Download |
| 49 | Lecture 49 : Construction of Confidence Ellipsoids - II | Download |
| 50 | Lecture 50 : Adversarial Contextual Bandits - I | Download |
| 51 | Lecture 51 : Adversarial Contextual Bandits II | Download |
| 52 | Lecture 52 : Exp4 Algorithm | Download |
| 53 | Lecture 53 : Regret of Exp4 | Download |
| 54 | Lecture 54 : Adversarial Linear Bandits | Download |
| 55 | Lecture 55 : Exp3 for Adversarial Linear Bandits | Download |
| 56 | Lecture 56 : Introduction to Pure Exploration and its lower bounds | Download |
| 57 | Lecture 57 : Uniform Exploration | Download |
| 58 | Lecture 58 : KL-LUCB | Download |
| 59 | Lecture 59 : Lil’ UCB | Download |
| 60 | Lecture 60 : Lower Bound for Pure Exploration Problem | Download |
| Sl.No | Chapter Name | English |
|---|---|---|
| 1 | Lecture 1: Introduction to Online Learning –I | Download Verified |
| 2 | Lecture 2: Introduction to Online Learning –I | Download Verified |
| 3 | Lecture 3: Basics of Statistical Learning | Download Verified |
| 4 | Lecture 4: Empirical risk minimization | Download Verified |
| 5 | Lecture 5: Consistency Halving algorithm | Download Verified |
| 6 | Lecture 6: Online Learnability | Download Verified |
| 7 | Lecture 7: Standard Optimal Algorithm | Download Verified |
| 8 | Lecture 8: Classification in unrealizability case | Download Verified |
| 9 | Lecture 9: Covers Impossibility Result | Download Verified |
| 10 | Lecture 10: Weighted Majority | Download Verified |
| 11 | Lecture 11: Proof Weighted Majority | Download Verified |
| 12 | Lecture 12: Full Information vs Bandit Setting | Download Verified |
| 13 | Lecture 13: Adversarial Bandit Setting | Download Verified |
| 14 | Lecture 14: Exponential Weights for Exploration and Exploitation Algorithm | Download Verified |
| 15 | Lecture 15: Regret Bound of Exp3 | Download Verified |
| 16 | Lecture 16: Regret Bound of Exp3(Contd.) | Download Verified |
| 17 | Lecture 17: Exp3.P and Exp3.IX | Download Verified |
| 18 | Lecture 18: Online Convex Optimisation | Download Verified |
| 19 | Lecture 19: Follow the Leader (FTL) Algorithm | Download Verified |
| 20 | Lecture 20: Follow the Regularized Leader | Download Verified |
| 21 | Lecture 21: Online Gradient Descent | Download Verified |
| 22 | Lecture 22: Strongly Convex Function | Download Verified |
| 23 | Lecture 23: FoReL with Strongly Convex Regulariser | Download Verified |
| 24 | Lecture 24: FoReL with Strongly Convex Regulariser (Contd.) | Download Verified |
| 25 | Lecture 25: Euclidean and Entropy Regularizer | Download Verified |
| 26 | Lecture 26: Introduction to Stochastic Bandits | Download Verified |
| 27 | Lecture 27: Concentration Inequalities | Download Verified |
| 28 | Lecture 28: Subgaussian Random Variable | Download Verified |
| 29 | Lecture 29: Regret Definition and Regret Decomposition | Download Verified |
| 30 | Lecture 30: Explore and Commit (ETC) Algorithm | Download Verified |
| 31 | Lecture 31: Regret Analysis and ETC | Download Verified |
| 32 | Lecture 32: Optimism in the Face of Uncertainty | Download Verified |
| 33 | Lecture 33: Upper Confidence Bound Algorithm | Download Verified |
| 34 | Lecture 34 : Regret Analysis of UCB | Download Verified |
| 35 | Lecture 35 : Problem Dependent and Independent Bounds of UCB | Download Verified |
| 36 | Lecture 36 : KL-UCB Algorithm | Download Verified |
| 37 | Lecture 37 : Thompson Sampling - Brief Discussion | Download Verified |
| 38 | Lecture 38 : Proof Idea of Lower Bounds - 1 | Download Verified |
| 39 | Lecture 39 : Proof Idea of Lower Bounds - 2 | Download Verified |
| 40 | Lecture 40 : Proof of Lower Bound-1 | Download Verified |
| 41 | Lecture 41 : Proof of Lower Bound-2 | Download Verified |
| 42 | Lecture 42 : Stochastic Contextual Bandits | Download Verified |
| 43 | Lecture 43 : Introduction to Stochastic Linear Bandits | Download Verified |
| 44 | Lecture 44 : Stochastic Linear Bandits | Download Verified |
| 45 | Lecture 45 : Regret Analysis of SLB-I | Download Verified |
| 46 | Lecture 46 : Regret Analysis of SLB - II | PDF unavailable |
| 47 | Lecture 47 : Regret Analysis of SLB-III | PDF unavailable |
| 48 | Lecture 48 : Construction of Confidence Ellipsoid - I | PDF unavailable |
| 49 | Lecture 49 : Construction of Confidence Ellipsoids - II | PDF unavailable |
| 50 | Lecture 50 : Adversarial Contextual Bandits - I | PDF unavailable |
| 51 | Lecture 51 : Adversarial Contextual Bandits II | PDF unavailable |
| 52 | Lecture 52 : Exp4 Algorithm | PDF unavailable |
| 53 | Lecture 53 : Regret of Exp4 | PDF unavailable |
| 54 | Lecture 54 : Adversarial Linear Bandits | PDF unavailable |
| 55 | Lecture 55 : Exp3 for Adversarial Linear Bandits | PDF unavailable |
| 56 | Lecture 56 : Introduction to Pure Exploration and its lower bounds | PDF unavailable |
| 57 | Lecture 57 : Uniform Exploration | PDF unavailable |
| 58 | Lecture 58 : KL-LUCB | PDF unavailable |
| 59 | Lecture 59 : Lil’ UCB | PDF unavailable |
| 60 | Lecture 60 : Lower Bound for Pure Exploration Problem | PDF unavailable |
| Sl.No | Language | Book link |
|---|---|---|
| 1 | English | Download |
| 2 | Bengali | Not Available |
| 3 | Gujarati | Not Available |
| 4 | Hindi | Not Available |
| 5 | Kannada | Not Available |
| 6 | Malayalam | Not Available |
| 7 | Marathi | Not Available |
| 8 | Tamil | Not Available |
| 9 | Telugu | Not Available |