Time and Location:
Monday, Wednesday 3:05-4:25pm, GHC 4307. See Logistics for more details.
Class Videos:
Class videos will be available on Panopto.
| Date | Event | Description | Announcements* | Materials | ||
|---|---|---|---|---|---|---|
| August 28 | Lecture 1 | Machine Learning: Introduction to Deep Learning | Lecture: slides, recordings | |||
| August 30 | Lecture 2 | Deep Learning Basics: the Perceptron | Lecture: slides, recordings | |||
| September 1 | - | - | ||||
| September 4 | Holiday | Labor Day | ||||
| September 6 | Lecture 3 | Neural Networks 1: Backpropagation | Lecture: slides, recordings | |||
| September 8 | Recitation 1 | Probability Distributions | Recitation: slides, recordings | |||
| September 11 | Lecture 4 | Neural Networks 2: Training Techniques | Lecture: slides, recording, Reading: Regularization for Deep Learning (Goodfellow) | |||
| September 13 | Lecture 5 | Neural Networks in Practice: Vision | HW1 out | Lecture: slides, recording, Reading: Deep Feedforward Networks (Goodfellow) (Ch. 6-6.4) | ||
| September 15 | Recitation 2 | Homework 1 | recording | |||
| September 18 | Lecture 6 | Neural Networks in Practice: Vision II | Lecture: slides, recording, Reading: Deep Feedforward Networks (Goodfellow) (Ch. 6.5-6.6), Optmization for Training Deep Models (Goodfellow) | |||
| September 20 | Lecture 7 | Neural Networks in Practice: Vision III | Lecture: slides, recording | |||
| September 22 | - | - | ||||
| September 25 | Lecture 8 | Vision Transformers | Lecture: slides, recording, Reading: Convolutional Networks (Goodfellow), CNNs for Visual Recognition (CS231n) | |||
| September 27 | Lecture 9 | Unsupervised Learning: Directed Graphical Models | Lecture: slides, recording, Reading: Bayesian Networks (Bishop) (Ch. 8.1-8.2) | |||
| September 29 | - | - | ||||
| October 2 | Lecture 10 | Undirected Graphical Models and Markov Random Fields (MRFs) | HW1 due | Lecture: slides, recording, Reading: MRFs (Bishop) (Ch. 8.3) | ||
| October 4 | Lecture 11 | RBMs and Deep Belief Networks | HW2 out | Lecture: slides, recording, Reading: Deep Generative Models (Goodfellow) (Ch. 20-20.9) | ||
| October 6 | Recitation 3 | Homework 2 | slides, recording | |||
| October 9 | Lecture 12 | Autoencoders/Sparse Coding Models | Lecture: slides, recording, Reading: Autoencoders (Goodfellow) (Ch. 14) | |||
| October 11 | Lecture 13 | Introduction to Language Modeling | Lecture: slides, recording | |||
| October 13 | Recitation 4 | PyTorch and AWS | Lecture: recording | |||
| October 16 | Holiday | Fall break | ||||
| October 18 | Holiday | Fall break | ||||
| October 20 | Holiday | Fall break | ||||
| October 23 | Lecture 14 | Sequence to Sequence Models, RNNs | Lecture: slides, recording | |||
| October 25 | Lecture 15 | Transformer 1: Self-attention layer | HW2 due | Lecture: slides, recording, Reading: The Illustrated Transformer (Alamnar) | ||
| October 27 | - | - | ||||
| October 30 | Lecture 16 | Transformer 2: Transformer Encoder and Transformer Decoder | Midway Report due | Lecture: slides, recording | ||
| November 1 | Lecture 17 | Transformer 3: Generative Models and the Road to AGI | HW3a out | Lecture: slides, recording, Reading: Sparks of Artificial General Intelligence | ||
| November 3 | Recitation 5 | Homework 3a | ||||
| November 6 | Lecture 18 | Variational Inference | Lecture: slides, recording, Reading: Approximate Inference (Goodfellow) (Ch. 19) | |||
| November 8 | Lecture 19 | Variational Autoencoders | Lecture: slides, recording, Reading: Deep Generative Models (Goodfellow) (Ch. 20.10.3), Tutorial on Variational Autoencoders (Doersch), Variational Autoencoders (Jordan) | |||
| November 10 | - | - | ||||
| November 13 | Lecture 20 | Generative Adversarial Networks, Normalizing Flows | HW3a due | Lecture: slides, recording, Reading: Deep Generative Models (Goodfellow) (Ch. 20.10.4), GANs (Ermon), Normalizing Flow Models (Ermon) | ||
| November 15 | Lecture 21 | Graph Neural Networks | HW3b out | Lecture: slides, recording, Reading: A Gentle Introduction to Graph Neural Networks | ||
| November 17 | Recitation 6 | Homework 3b | Lecture: recording | |||
| November 20 | Lecture 22 | Diffusion Models | Lecture: slides, recording, Reading: Understanding Diffusion Models: A Unified Perspective | |||
| November 22 | Holiday | Thanksgiving | ||||
| November 24 | Holiday | Thanksgiving | ||||
| November 27 | Lecture 23 | Multi-Modal Learning | Lecture: slides, recording | |||
| November 29 | Lecture 24 | Embodied AI: Language and Perception | HW3b due | Lecture: slides, recording | ||
| December 1 | - | - | ||||
| December 4 | Lecture 25 | Reinforcement Learning | Final Report Due | Lecture: slides, | ||
| December 6 | Lecture 26 | Transformer 4: Scaling Law in Transformer Models | ||||
| December 8 | - | - |
* all announcement dates are tentative and subject to change