The accompanying material for each lecture is posted here.

Lecture 1: Introduction

Origins of deep learning, course goals, overview of machine-learning paradigms, intro to computational acceleration.

Lecture 2: Supervised learning

Supervised learning problem statement, data sets, hypothesis classes, loss functions, basic examples of supervised machine learning models, adding non-linear...

Lecture 3: Neural Networks

Linear and multilayer Perceptron, loss functions, activation functions, pooling, weight sharing, convolutional layers, gradient descent, backpropagation.

Lecture 4: Training Neural Networks

Approximation, estimation and optimization errors, regularization, loss surface curvature, descent-based optimization methods, second-order methods.

Lecture 5: Sequence Models

RNN model, input-output sequences relationships, non-sequential input, layered RNN, backpropagation through time, word embeddings, attention, transformers.

Lecture 7: Reinforcement Learning

Markov decision process, policies, rewards, value functions, the Bellman equation, q-learning, policy learning, actor-critic learning, AutoML.

Additional Resources

  • The supplemental material page contains prerequisite topics you should be familiar with.

  • Detailed notes will be available for most lectures on the lecture notes page.

  • You can browse the course youtube playlist from the current and previous semester. New videos will be posted on the site.