Fall 2020 CS 498 Introduction to Deep Learning

Quick links: schedule, Compass2g (grades), Piazza (announcements, discussion board), course policies,
lecture videos

This course will provide an elementary hands-on introduction to neural networks and deep learning. Topics covered will include: linear classifiers; multi-layer neural networks; back-propagation and stochastic gradient descent; convolutional neural networks and their applications to computer vision tasks like object detection and dense image labeling; recurrent neural networks and state-of-the-art sequence models like transformers; generative models (generative adversarial networks and variational autoencoders); and deep reinforcement learning. Coursework will consist of programming assignments in Python (primarily PyTorch). Those registered for 4 credit hours will have to complete a project.

Instructor: Svetlana Lazebnik (slazebni -at- illinois.edu)

Lectures: Wednesdays and Fridays, 3:30PM-4:45PM
Lectures will be delivered live over Zoom and recorded for later asynchronous viewing. Access will be restricted to students logged into the illinois.edu domain. Please check Piazza for links.

TAs: Aiyu Cui (aiyucui2), Adam Stewart (adamjs5), Junting Wang (junting3), Jeffrey Zhang (jz41), Shivani Kamtikar (skk7)

Instructor and TA office hours: See Piazza (and always check for any last-minute announcements of changes)

Contacting the course staff: For emergencies and special circumstances, please email the instructor. For questions about lectures and assignments, use Piazza. For questions about your scores (including regrade requests), email the responsible TAs.

Prerequisites: Multi-variable calculus, linear algebra, data structures (CS 225 or equivalent), CS 361 or STAT 400. No previous exposure to machine learning is required.

Grading scheme:

  Be sure to read the course policies!

Schedule (tentative)

Date Topic Assignments
August 26 Introduction: PPTX, PDF Self-study: Python/numpy tutorial
August 28 Intro to learning and classifiers: PPTX, PDF  
September 2 Linear classifiers: PPTX, PDF  
September 4 Linear classifiers cont. (see slides above)  
September 9 Multi-class classification: PPTX, PDF Assignment 1 out
September 11 Nonlinear classifiers, bias-variance tradeoff: PPTX, PDF  
September 16 Backpropagation: PPTX, PDF  
September 18 Convolutional networks: PPTX, PDF Assignment 1 due September 22
September 23 Convolutional networks cont. (see slides above) Assignment 2 out
September 25 Advanced training: PPTX, PDF
September 30 PyTorch tutorial: Jupyter Notebook
October 2 Object detection: PPTX, PDF Assignment 2 due October 6
October 7 Object detection cont. Assignment 3 out: Part 1, Part 2
October 9 Dense prediction: PPTX, PDF  
October 14 Dense prediction cont. Project proposals due (for 4 credits)
October 16 Self-supervised learning: PPTX, PDF  
October 21 Visualization: PPTX, PDF  
October 23 Adversarial examples: PPTX, PDF  
October 28 Generative adversarial networks: PPTX, PDF  
October 30 Conditional GANs: PPTX, PDF  
November 4 Variational autoencoders: PPTX, PDF Assignment 4 out
Assignment 3 due November 5
November 6 Recurrent networks: PPTX, PDF  
November 11 Sequence-to-sequence models with attention: PPTX, PDF  
November 13 Transformers: PPTX, PDF Project progress reports due November 16
November 18 Deep Q-learning: PPTX, PDF Assignment 4 due November 23
November 20 Policy gradient methods: PPTX, PDF Assignment 5 out,
optional extra credit assignment out
December 2 Deep RL applications and challenges: PPTX, PDF  
December 4 Deep learning trends: PPTX, PDF  
December 9 Societal impacts and ethics: PPTX, PDF Assignment 5 due December 9
Final project reports due December 14


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