Recordings of my Lectures. See also my main channel https://www.youtube.com/@MihaiNicaMath .

Multiarm Bandits: explore-then-exploit, Upper Confidence Bounds (UCB) Algorithms | Intro to RL
1:06:07
Multiarm Bandits: explore-then-exploit, Upper Confidence Bounds (UCB) Algorithms | Intro to RL
Monte Carlo Tree Search and AlphaGo Zero | Intro to Reinforcement Learning
1:03:42
Monte Carlo Tree Search and AlphaGo Zero | Intro to Reinforcement Learning
Adaptive Exploration using Softmax, Follow the Perterbed Leader and Intro to AlphaZero | Intro to RL
1:11:36
Adaptive Exploration using Softmax, Follow the Perterbed Leader and Intro to AlphaZero | Intro to RL
Neural Network in JAX and Introduction to MultiArm bandits | Intro to Reinforcement Learning
1:09:46
Neural Network in JAX and Introduction to MultiArm bandits | Intro to Reinforcement Learning
Vectorizing Tic Tac Toe to run games in parallel and intro to Neural Networks in JAX | Intro to RL
49:52
Vectorizing Tic Tac Toe to run games in parallel and intro to Neural Networks in JAX | Intro to RL
Temporal Difference learnings deadly triad, and intro array broadcasting jax.vmap | Intro to RL
1:09:48
Temporal Difference learnings deadly triad, and intro array broadcasting jax.vmap | Intro to RL
More Temporal Difference Learning: SARSA, Q-Learning, and learning gradients TD(lambda) | Intro RL
1:04:16
More Temporal Difference Learning: SARSA, Q-Learning, and learning gradients TD(lambda) | Intro RL
Intro to Temporal Difference Learning | Intro to Markov chains and Reinforcement Learning
1:12:12
Intro to Temporal Difference Learning | Intro to Markov chains and Reinforcement Learning
Pig-Tac-Toe Game, Coarse Coding, Gradient Descent Monte Carlo | Intro to RL
1:07:18
Pig-Tac-Toe Game, Coarse Coding, Gradient Descent Monte Carlo | Intro to RL
Learning with Parameters: Intro to Gradient Descent in JAX | Intro to Reinforcement Learning
1:11:59
Learning with Parameters: Intro to Gradient Descent in JAX | Intro to Reinforcement Learning
The Explore Problem in RL: Optimism, Random Starts, Epsilon Greedy and Off policy learning
1:12:31
The Explore Problem in RL: Optimism, Random Starts, Epsilon Greedy and Off policy learning
Intro to Monte Carlo Learning on Zombie Dice (sorry the digital blackboard wasn't working!) | RL Int
1:12:42
Intro to Monte Carlo Learning on Zombie Dice (sorry the digital blackboard wasn't working!) | RL Int
Gambler's Problem Solved with Value Iteration and Intro to Monte Carlo Methods | Intro to RL
1:18:07
Gambler's Problem Solved with Value Iteration and Intro to Monte Carlo Methods | Intro to RL
np.indices, Multi-Round & 2 Player PIG Solved by Value Iteration, The Gamblers Problem | Intro to RL
58:19
np.indices, Multi-Round & 2 Player PIG Solved by Value Iteration, The Gamblers Problem | Intro to RL
Solution to dice game PIG using the Bellman equation in Numpy | Intro to Markov Chains and RL
1:16:54
Solution to dice game PIG using the Bellman equation in Numpy | Intro to Markov Chains and RL
How to model the dice game PIG, value and q functions, and the Bellman Equation | Intro to RL
56:31
How to model the dice game PIG, value and q functions, and the Bellman Equation | Intro to RL
Drunkard's Walk Solved with Value Iteration and Dice Game Pig Part 1 | Intro to Markov Chains and RL
1:02:07
Drunkard's Walk Solved with Value Iteration and Dice Game Pig Part 1 | Intro to Markov Chains and RL
Value Iteration for Markov Chains and Intro to the Drunkards Walk | Intro to RL
1:10:07
Value Iteration for Markov Chains and Intro to the Drunkards Walk | Intro to RL
How many coin flips to HH and the (baby) Bellman Equation | Intro to Markov Chains and RL
1:11:51
How many coin flips to HH and the (baby) Bellman Equation | Intro to Markov Chains and RL
How many rolls to reach the end in snakes and ladders?
1:10:46
How many rolls to reach the end in snakes and ladders?
Snakes and Ladders part 2: Matrix Implementation in NumPy | Intro to RL
1:17:44
Snakes and Ladders part 2: Matrix Implementation in NumPy | Intro to RL
Intro to Reinforcement Learning Example 1: Snakes and Ladders
58:29
Intro to Reinforcement Learning Example 1: Snakes and Ladders
What is Reinforcement Learning? (and what is it NOT?) | Intro to Markov Chains and RL W25
1:11:05
What is Reinforcement Learning? (and what is it NOT?) | Intro to Markov Chains and RL W25
Central Limit Theorem (Rolling 24 dice), Gamblers Fallacy and Simpsons Paradox | Intro Probability
41:22
Central Limit Theorem (Rolling 24 dice), Gamblers Fallacy and Simpsons Paradox | Intro Probability
Using vector embeddings for tasks letter2vec example | Intro to Data Science
1:11:04
Using vector embeddings for tasks letter2vec example | Intro to Data Science
Modifying the Loss Function to do Regularization and Autoencoders | Intro to Data Science
1:04:16
Modifying the Loss Function to do Regularization and Autoencoders | Intro to Data Science
Standard Deviations are like Pythagoras and the Central Limit Theorem | Intro to Probability
34:35
Standard Deviations are like Pythagoras and the Central Limit Theorem | Intro to Probability
Subtracting/Dividing Random Variables to Standardize them and some R | Intro to Probability
45:05
Subtracting/Dividing Random Variables to Standardize them and some R | Intro to Probability
MNIST tutorial in JAX part 2 From CrossEntropy loss to MLP | Intro to Data Science 11-20
1:12:48
MNIST tutorial in JAX part 2 From CrossEntropy loss to MLP | Intro to Data Science 11-20
Calculating normal/Gaussian probabilities using tables | Intro to Probability 11-20
44:22
Calculating normal/Gaussian probabilities using tables | Intro to Probability 11-20