Lectures

Zoom link for lectures

The lecture presentations and the accompanying material for each lecture is posted here.

Lecture 1: Introduction

Origins of deep learning, course goals, overview of machine-learning paradigms.

Lecture 2: Multi Layer Perceptron

MLP as non linear deep model Intro to CNN

Lecture 3: Convolution Neural Nets

This lecture is about the revolutional Convolutional neural networks

Lecture 4+5: optimization

This lecture is about deep neural network optimization.

Lecture 6: Sequence Models

This lecture is about deep neural networks for sequence processing.

Lecture 7: Attention

This lecture is about the attention mechanism.

Lecture 8+9: Transformers

Transformers, LLM and ViT

Lecture 10: Generative Adverserial Networks

Video

Lecture 11: Diffusion Models

Video here

Lecture 12: Multi-Modality

Video

Additional Resources

• From previus semesters, the videos of Alex lectures

• 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.