Spring 2021 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: 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.

TAs: Adam Stewart (adamjs5), Junting Wang (junting3), Jyoti Aneja (janeja2), Licheng Luo (ll6)

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:

  • Programming assignments: 80% of the grade for 3-credit students and 60% of the grade for 4-credit students
  • Quizzes: 20% of the grade for 3-credit students or 15% for 4-credit students
    • Four online multiple choice quizzes throughout the semester (will be conducted on Compass, you will have three or four days in which each quiz can be completed, though you will have a limited amount of time once you start)
  • Project: 25% of the grade for 4-credit students
  • Participation extra credit: up to 3% bonus on the cumulative course score will be offered for active in-class and Piazza participation
  Be sure to read the course policies!

Schedule (tentative)

Date Topic Assignments
January 25 Introduction: PPTX, PDF Self-study: Python/numpy tutorial
January 27 Intro to learning and classifiers: PPTX, PDF  
February 1 Linear classifiers: PPTX, PDF  
February 3 Linear classifiers cont.  
February 8 Multi-class classification: PPTX, PDF Assignment 1 out
February 10 Nonlinear classifiers: PPTX, PDF  
February 15 Backpropagation: PPTX, PDF  
February 22 Convolutional networks: PPTX, PDF Assignment 1 due February 23
February 24 Convolutional networks cont. Assignment 2 out
March 1 Advanced training: PPTX, PDF
March 3 PyTorch tutorial: Jupyter notebook
March 8 Advanced trainining cont. Assignment 2 due March 9
March 10 Object detection: PPTX, PDF Assignment 3 Part 1 out
March 15 Object detection cont. Assignment 3 Part 2 out
March 17 Dense prediction: PPTX, PDF Project proposals due (for 4 credits)
March 22 Self-supervised learning: PPTX, PDF  
March 29 Visualization: PPTX, PDF  
March 31 Adversarial examples: PPTX, PDF  
April 5 Generative adversarial networks: PPTX, PDF  
April 7 Conditional GANs: PPTX, PDF Assignment 4 out
Assignment 3 due April 8
April 12 Variational autoencoders: PPTX, PDF  
April 14 Recurrent networks: PPTX, PDF Project progress reports due April 15
April 19 Sequence-to-sequence models with attention: PPTX, PDF  
April 21 Transformers: PPTX, PDF
April 26 Deep Q-learning: PPTX, PDF Assignment 4 due April 27
April 28 Policy gradient methods: PPTX, PDF Assignment 5 out,
optional extra credit assignment out
May 3 Deep RL applications and challenges: PPTX, PDF  
May 5 Societal impacts and ethics: PPTX, PDF Assignment 5, extra credit due May 11
Final project reports due May 13


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