1:56:11Session 1 Recap on Random Variables, Exemplar Discrete Distributions, Expectations
A 116-minute video from Mainak's PMRF Tutorials: “Session 1 Recap on Random Variables, Exemplar Discrete Distributions, Expectations”.
Mainak's PMRF Tutorials
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1:56:11
2:11:34Session 10: Gradient descent, why it works, Linear and Logistic regression, ML estimate
A 132-minute video from Mainak's PMRF Tutorials: “Session 10: Gradient descent, why it works, Linear and Logistic regression, ML estimate”.
1:51:52Session 10: Stochastic Shortest Path, Bellman Operators, Proof of convergence of Policy Evaluation
A 112-minute video from Mainak's PMRF Tutorials: “Session 10: Stochastic Shortest Path, Bellman Operators, Proof of convergence of Policy Evaluation”.
1:44:33Session 11 Model Free Methods, Monte Carlo, Temporal Difference Algorithm, TD(λ) Algorithm
A 105-minute video from Mainak's PMRF Tutorials: “Session 11 Model Free Methods, Monte Carlo, Temporal Difference Algorithm, TD(λ) Algorithm”.
1:49:15Session 12: On Policy vs Off Policy Algorithms, Importance Sampling, Model-free Q learning, SARSA
A 109-minute video from Mainak's PMRF Tutorials: “Session 12: On Policy vs Off Policy Algorithms, Importance Sampling, Model-free Q learning, SARSA”.
1:45:21Session 13: Function Approximation in RL, Policy Evaluation, SGD Monte Carlo, TD(0) Implementation
A 105-minute video from Mainak's PMRF Tutorials: “Session 13: Function Approximation in RL, Policy Evaluation, SGD Monte Carlo, TD(0) Implementation”.
1:54:39Session 14: TD0 with linear function approximation, Glimpse at Stochastic Approximation Algorithm(1)
A 115-minute video from Mainak's PMRF Tutorials: “Session 14: TD0 with linear function approximation, Glimpse at Stochastic Approximation Algorithm(1)”.
1:52:56Session 15 TD(0) convergence proof (contd), Point of Convergence of TD(0) (linear function approx.)
A 113-minute video from Mainak's PMRF Tutorials: “Session 15 TD(0) convergence proof (contd), Point of Convergence of TD(0) (linear function approx.)”.
1:42:49Session 16 γ contraction, Banach's Fixed Point Theorem, How far is it far from the intended optimal
A 103-minute video from Mainak's PMRF Tutorials: “Session 16 γ contraction, Banach's Fixed Point Theorem, How far is it far from the intended optimal”.
1:39:10Session 17: Off-Policy Evaluation of TD0 with linear function Approximation, Emphatic TD0
A 99-minute video from Mainak's PMRF Tutorials: “Session 17: Off-Policy Evaluation of TD0 with linear function Approximation, Emphatic TD0”.
1:57:08Session 18 Synchronous Q-learning, Model-free, based, tabular, with Linear Fn. Approx., Convergence
A 117-minute video from Mainak's PMRF Tutorials: “Session 18 Synchronous Q-learning, Model-free, based, tabular, with Linear Fn. Approx., Convergence”.
1:54:17Session 19: Asynchronous Q learning, Classification in ML, MLE, Logistic and Softmax Regression
A 114-minute video from Mainak's PMRF Tutorials: “Session 19: Asynchronous Q learning, Classification in ML, MLE, Logistic and Softmax Regression”.
2:29:27Session 1: Introduction to counting, RVs, and distributions
A 149-minute video from Mainak's PMRF Tutorials: “Session 1: Introduction to counting, RVs, and distributions”.
1:50:03Session 20: Deep Neural Networks, MLP, Backpropagation, Policy Gradient, REINFORCE
A 110-minute video from Mainak's PMRF Tutorials: “Session 20: Deep Neural Networks, MLP, Backpropagation, Policy Gradient, REINFORCE”.
1:48:51Session 21: Actor Critic based Policy Gradient, Safe RL, Planning, DYNA, Curriculum Learning
A 109-minute video from Mainak's PMRF Tutorials: “Session 21: Actor Critic based Policy Gradient, Safe RL, Planning, DYNA, Curriculum Learning”.
3:02:06Session 2: Discrete random variables, distribution, expectation, lotus, variance
A 182-minute video from Mainak's PMRF Tutorials: “Session 2: Discrete random variables, distribution, expectation, lotus, variance”.
1:48:30Session 2: Recap - Continuous Distributions, Transformation of random variables
A 108-minute video from Mainak's PMRF Tutorials: “Session 2: Recap - Continuous Distributions, Transformation of random variables”.
1:54:27Session 3: Recap on Joint Distributions, Conditional Distributions, and Conditional Expectations
A 114-minute video from Mainak's PMRF Tutorials: “Session 3: Recap on Joint Distributions, Conditional Distributions, and Conditional Expectations”.
2:34:46Session 3: continuous rvs, pmf, pdf, inequality, Uniform, Exponential, Normal, transformation of rvs
A 155-minute video from Mainak's PMRF Tutorials: “Session 3: continuous rvs, pmf, pdf, inequality, Uniform, Exponential, Normal, transformation of rvs”.
1:42:57Session 4: Introduction to Reinforcement Learning, Multi-armed Bandits Algorithm and Implementation
A 103-minute video from Mainak's PMRF Tutorials: “Session 4: Introduction to Reinforcement Learning, Multi-armed Bandits Algorithm and Implementation”.
3:15:08Session 4: MGFs, random vectors, joint distribution, random process, Random walks, Markov chains
A 195-minute video from Mainak's PMRF Tutorials: “Session 4: MGFs, random vectors, joint distribution, random process, Random walks, Markov chains”.
1:50:48Session 5 ODE Interpretation in Bandits, UCB, Gradient-Based Algorithms, UCB in Python
A 111-minute video from Mainak's PMRF Tutorials: “Session 5 ODE Interpretation in Bandits, UCB, Gradient-Based Algorithms, UCB in Python”.
2:45:29Session 5: Intro to Matrices, vector spaces, span and basis, 4-fundamental subspaces, elimination
A 165-minute video from Mainak's PMRF Tutorials: “Session 5: Intro to Matrices, vector spaces, span and basis, 4-fundamental subspaces, elimination”.
1:53:14Session 6 Random Processes, Markov Chains and Stationary Distribution
A 113-minute video from Mainak's PMRF Tutorials: “Session 6 Random Processes, Markov Chains and Stationary Distribution”.
2:52:23Session 6: Projections, Least squares, eigenvalue-eigenvectors, Char. polynomial, similar matrices
A 172-minute video from Mainak's PMRF Tutorials: “Session 6: Projections, Least squares, eigenvalue-eigenvectors, Char. polynomial, similar matrices”.
2:43:02Session 7: Eigenvector decomposition, unitary and normal matrices, Application: PCA and SVD
A 163-minute video from Mainak's PMRF Tutorials: “Session 7: Eigenvector decomposition, unitary and normal matrices, Application: PCA and SVD”.
1:51:33Session 7: MDPs, Action, Value, Reward functions, Bellman Equations 1, Examples
A 112-minute video from Mainak's PMRF Tutorials: “Session 7: MDPs, Action, Value, Reward functions, Bellman Equations 1, Examples”.
1:48:24Session 8 Bellman Equation, Optimal Policy, Iterative Policy Evaluation, Policy & Value Iteration
A 108-minute video from Mainak's PMRF Tutorials: “Session 8 Bellman Equation, Optimal Policy, Iterative Policy Evaluation, Policy & Value Iteration”.
2:43:07Session 8: Inner products, vector norms, dual spaces, introduction to matrix norms
A 163-minute video from Mainak's PMRF Tutorials: “Session 8: Inner products, vector norms, dual spaces, introduction to matrix norms”.
2:41:16Session 9: Introduction to convex functions, Jensen’s, Holder’s inequality, Minkowski, Lagrangian
A 161-minute video from Mainak's PMRF Tutorials: “Session 9: Introduction to convex functions, Jensen’s, Holder’s inequality, Minkowski, Lagrangian”.
1:55:36Session 9: Policy Iteration & Q learning code, Finite Horizon MDPs, Dynamic Program, Theory and Exmp
A 116-minute video from Mainak's PMRF Tutorials: “Session 9: Policy Iteration & Q learning code, Finite Horizon MDPs, Dynamic Program, Theory and Exmp”.