Courses

Yaser Abu-Mostafa's famed Caltech course on statistical learning theory: Hoeffding's inequality, VC dimension, bias-variance tradeoff, regularization, support vector machines, and neural nets.

Foundational reinforcement learning course taught by AlphaGo lead researcher David Silver: Markov Decision Processes, Bellman optimality, dynamic programming, Monte Carlo, TD learning, and function approximation.

Rigorous graduate treatment of modern deep learning foundations at MIT, covering optimization dynamics, representation learning, sequence modeling, generative architectures, and generalization theory.

From scalar autograd and backpropagation in pure Python up to modern Transformer language models built line-by-line from scratch.

Linear classifiers, computational graphs, backpropagation, convolutional architectures, batch normalization, training dynamics, object detection, semantic segmentation, and generative visual models.

Uninformed and A* heuristic search, constraint satisfaction, adversarial minimax/expectimax games, Markov decision processes, reinforcement learning, Bayes nets, and particle filtering.

Imitation learning, policy gradients, actor-critic algorithms, value-function Q-learning, model-based RL, offline reinforcement learning, exploration, and inverse RL.

Research seminar examining self-attention architectures across language modeling, multimodal reasoning, robotics, biology, and scaling laws with leading industry and academic pioneers.