Mainak's PMRF Tutorials

Session 4 Diffusion Denoising Probabilistic Model DDPM

Mainak's PMRF Tutorials

Session 3 Beta VAE, Info VAE, Implementing-VAE

Mainak's PMRF Tutorials

Session 2 Variational Inference, Autoencoders, Variational Autoencoders

Mainak's PMRF Tutorials

Session1 GenAI, Latent Variable, EM, GMMs

Mainak's PMRF Tutorials

Session 21 Actor Critic based Policy Gradient, Safe RL, Planning, DYNA, Curriculum Learning

Mainak's PMRF Tutorials

Session 20 Deep Neural Networks, MLP, Backpropagation, Policy Gradient, REINFORCE

Mainak's PMRF Tutorials

Session 19 Asynchronous Q learning, Classification in ML, MLE, Logistic and Softmax Regression

Mainak's PMRF Tutorials

Session 18 Synchronous Q-learning, Model-free, based, tabular, with Linear Fn. Approx., Convergence

Mainak's PMRF Tutorials

Session 17 Off-Policy Evaluation of TD0 with linear function Approximation, Emphatic TD0

Mainak's PMRF Tutorials

Session 16 γ contraction, Banach's Fixed Point Theorem, How far is it far from the intended optimal

Mainak's PMRF Tutorials

Session 15 TD(0) convergence proof (contd), Point of Convergence of TD(0) (linear function approx.)

Mainak's PMRF Tutorials

Session 14 TD0 with linear function approximation, Glimpse at Stochastic Approximation Algorithm(1)

Mainak's PMRF Tutorials

Session 13 Function Approximation in RL, Policy Evaluation, SGD Monte Carlo, TD(0) Implementation

Mainak's PMRF Tutorials

Session 12 On Policy vs Off Policy Algorithms, Importance Sampling, Model-free Q learning, SARSA

Mainak's PMRF Tutorials

Session 11 Model Free Methods, Monte Carlo, Temporal Difference Algorithm, TD(λ) Algorithm

Mainak's PMRF Tutorials

Session 10 Stochastic Shortest Path, Bellman Operators, Proof of convergence of Policy Evaluation

Mainak's PMRF Tutorials

Session 9 Policy Iteration & Q learning code, Finite Horizon MDPs, Dynamic Program, Theory and Exmp

Mainak's PMRF Tutorials

Session 8 Bellman Equation, Optimal Policy, Iterative Policy Evaluation, Policy & Value Iteration

Mainak's PMRF Tutorials

Session 7 MDPs, Action, Value, Reward functions, Bellman Equations 1, Examples

Mainak's PMRF Tutorials

Session 6 Random Processes, Markov Chains and Stationary Distribution

Mainak's PMRF Tutorials

Session 5 ODE Interpretation in Bandits, UCB, Gradient-Based Algorithms, UCB in Python

Mainak's PMRF Tutorials

Session 4 Introduction to Reinforcement Learning, Multi-armed Bandits Algorithm and Implementation

Mainak's PMRF Tutorials

Session 3 Recap on Joint Distributions, Conditional Distributions, and Conditional Expectations

Mainak's PMRF Tutorials

Session 2 Recap - Continuous Distributions, Transformation of random variables

Mainak's PMRF Tutorials

Session 1 Recap on Random Variables, Exemplar Discrete Distributions, Expectations

Mainak's PMRF Tutorials

Session 24 Mixture Models, Expectation Maximization, GMMs, K-means is a specialized GMM

Mainak's PMRF Tutorials

Session 23 Dimensionality Reduction - Principal Component Analysis, Linear Discriminant Analysis

Mainak's PMRF Tutorials

Session 22 Unsupervised Learning, Clustering algorithms, K-means, K-medoids, and Hierarchical

Mainak's PMRF Tutorials

Session 21 Backpropagation, Dropout, Bias-variance tradeoff, Prevent overfitting or underfitting

Mainak's PMRF Tutorials

Session 20 Perceptron, Perceptron Learning Algorithm, Convergence Proof, MLPs, Forward Propagation

Mainak's PMRF Tutorials