Spring 2021 CS 498 Introduction to Deep Learning

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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: Mondays and Wednesdays, 11:00AM-12:15PM
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.


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
January 25 Introduction Self-study: Python/numpy tutorial
January 27 Intro to learning and classifiers  
February 1 Linear classifiers  
February 3 Linear classifiers cont.  
February 8 Multi-class classification Assignment 1 out
February 10 Nonlinear classifiers, bias-variance tradeoff  
February 15 Backpropagation  
February 22 Convolutional networks Assignment 1 due
February 24 Convolutional networks cont. Assignment 2 out
March 1 Advanced training
March 3 PyTorch tutorial
March 8 Object detection Assignment 2 due
March 10 Object detection cont. Assignment 3 out
March 15 Dense prediction  
March 17 Dense prediction cont. Project proposals due (for 4 credits)
March 22 Self-supervised learning  
March 29 Visualization  
March 31 Adversarial examples  
April 5 Generative adversarial networks  
April 7 Conditional GANs  
April 12 Variational autoencoders Assignment 4 out
Assignment 3 due
April 14 Recurrent networks Project progress reports due
April 19 Sequence-to-sequence models with attention  
April 21 Transformers
April 26 Deep Q-learning Assignment 4 due
April 28 Policy gradient methods Assignment 5 out
May 3 Deep RL applications and challenges  
May 5 Societal impacts and ethics Assignment 5 due
Final project reports due


Other deep learning courses with useful materials


Useful textbooks available online

Statement on mental health

Diminished mental health, including significant stress, mood changes, excessive worry, substance/alcohol abuse, or problems with eating and/or sleeping can interfere with optimal academic performance, social development, and emotional wellbeing. The University of Illinois offers a variety of confidential services including individual and group counseling, crisis intervention, psychiatric services, and specialized screenings at no additional cost. If you or someone you know experiences any of the above mental health concerns, it is strongly encouraged to contact or visit any of the University’s resources provided below. Getting help is a smart and courageous thing to do -- for yourself and for those who care about you.

Counseling Center: 217-333-3704, 610 East John Street Champaign, IL 61820

McKinley Health Center:217-333-2700, 1109 South Lincoln Avenue, Urbana, Illinois 61801