This is an introductory course on deep learning. This course is designed for any graduate-level students with some background in Calculus, Linear Algebra and Probability.
The topics include, but not limited to, machine learning, deep neural network, regularization, optimization, graph learning and generative models.
The students who successfully finish this course will gain knowledge on representation learning via deep learning.

Calculus, Linear Algebra, Probability Theory

Quiz: 30%
Assignments: 15%
Final Project: 50%
Class participation: 5%

*The criteria above is subject to change.

https://udlbook.github.io/udlbook/

Chapter 1 - Introduction
Chapter 2 - Supervised learning
Chapter 3 - Shallow neural networks
Chapter 4 - Deep neural networks
Chapter 5 - Loss functions
Chapter 6 - Training models
Chapter 7 - Gradients and initialization
Chapter 8 - Measuring performance
Chapter 9 - Regularization
Chapter 10 - Convolutional networks
Chapter 11 - Residual networks
Chapter 12 - Transformers
Chapter 13 - Graph neural networks
Chapter 14 - Unsupervised learning
Chapter 15 - Generative adversarial networks
Chapter 16 - Normalizing flows
Chapter 17 - Variational autoencoders
Chapter 18 - Diffusion models
Chapter 19 - Deep reinforcement learning
Chapter 20 - Why does deep learning work?


*The course schedule is subject to change.

The course will be taught offline, face-to-face.
There will be two guest lectures from the authors of publications at top-tier AI venues.