February 7, 2020

290 words 2 mins read



STAT 453: Intro to Deep Learning @ UW-Madison (Spring 2020)

repo name rasbt/stat453-deep-learning-ss20
repo link https://github.com/rasbt/stat453-deep-learning-ss20
homepage http://pages.stat.wisc.edu/~sraschka/teaching/stat453-ss2020/
language Jupyter Notebook
size (curr.) 80498 kB
stars (curr.) 282
created 2020-01-20
license MIT License

STAT 453: Introduction to Deep Learning and Generative Models

Course Website: http://pages.stat.wisc.edu/~sraschka/teaching/stat453-ss2020/

Topics Summary (Planned)

Below is a list of the topics I am planning to cover. Note that while these topics are numerated by lectures, note that some lectures are longer or shorter than others. Also, we may skip over certain topics in favor of others if time is a concern. While this section provides an overview of potential topics to be covered, the actual topics will be listed in the course calendar at the bottom of the course website.

Part 1: Introduction

Part 2: Mathematical and computational foundations

Part 3: Introduction to neural networks

Part 4: Deep learning for computer vision and language modeling

  • Introduction to convolutional neural networks 1
  • Introduction to convolutional neural networks 2
  • Introduction to recurrent neural networks 1
  • Introduction to recurrent neural networks 2
  • Midterm exam

Part 5: Deep generative models

  • Autoencoders
  • Autoregressive models
  • Variational autoencoders
  • Normalizing Flow Models
  • Generative adversarial networks 1
  • Generative adversarial networks 2
  • Evaluating generative models

Part 6: Class projects and final exam

  • Course summary
  • Student project presentations 1
  • Student project presentations 2
  • Student project presentations 3
  • Final exam
  • Final report (online submission)
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