Spring 2021 CS 498 Introduction to Deep Learning
Quick links: schedule,
Piazza (announcements, discussion board),
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.
Be sure to read the course policies!
- 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
||Self-study: Python/numpy tutorial
||Intro to learning and classifiers
||Linear classifiers cont.
||Assignment 1 out
||Nonlinear classifiers, bias-variance tradeoff
||Assignment 1 due
||Convolutional networks cont.
||Assignment 2 out
||Assignment 2 due
||Object detection cont.
||Assignment 3 out
||Dense prediction cont.
||Project proposals due (for 4 credits)
||Generative adversarial networks
||Assignment 4 out|
Assignment 3 due
||Project progress reports due
||Sequence-to-sequence models with attention
||Assignment 4 due
||Policy gradient methods
||Assignment 5 out
||Deep RL applications and challenges
||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