Spring 2023 CS 444 Deep Learning for Computer Vision
Quick links: schedule,
assignment submission, quizzes, grades,
announcements and discussion,
Erik Desmazieres, The Library of Babel
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; generative models (generative adversarial networks and diffusion models); sequence models like recurrent networks and transformers; applications of transformers for language and vision; 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
3039 Campus Instructional Facility
Lectures will be recorded for later asynchronous viewing. Access to recordings will be restricted to students logged into the illinois.edu domain.
Daniel McKee (dbmckee2), Zitong Zhan (zitongz3), Nikash Walia (nikashw2), James Soole (soole2), Ryan Marten (marten4), Haomeng Zhang (haomeng4)
Instructor and TA office hours: See Campuswire (and always check announcements 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 the discussion board. For questions about your scores (including regrade requests), email the responsible TAs.
Prerequisites: Multi-variable calculus, linear algebra, data structures (CS 225 or equivalent), statistics (CS 361, STAT 400, or equivalent). 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 Canvas, you will have 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 discussion board participation
||Past and present of DL: PPTX, PDF
||Self-study: Python/numpy tutorial
||Overview of DL problems: PPTX, PDF
||Linear classifiers: PPTX, PDF
||Linear classifiers cont. (slides above)
||Linear classifiers cont.: PPTX, PDF
||Assignment 1 is out
||Nonlinear classifiers: PPTX, PDF
||Nonlinear classifiers cont.
||Backpropagation: PPTX, PDF
||Assignment 1 due February 14
||Convolutional networks: PPTX, PDF
||Assignment 2 is out
||Convolutional architectures: PPTX, PDF
||Convolutional architectures cont.
||Training in detail: PPTX, PDF
||PyTorch tutorial: Jupyter notebook
||Object detection: PPTX, PDF
||Assignment 2 due March 6
||Dense prediction: PPTX, PDF
||Project proposals due March 10|
Assignment 3 is out
||Self-supervised learning: PPTX, PDF
||Self-supervised learning cont.
||Adversarial examples: PPTX, PDF
||Generative adversarial networks: PPTX, PDF
||Advanced GAN models: PPTX, PDF
||Assignment 3 due April 4
||Advanced GAN models cont.
||Assignment 4 is out
||Image-to-image translation: PPTX, PDF
||Diffusion models: PPTX, PDF
||Project progress reports due April 14
||Deep Q-learning: PPTX, PDF
||Policy gradient methods: PPTX, PDF
||Assignment 4 due April 19|
extra credit assignment are out
||Recurrent networks: PPTX, PDF
||Models with attention, transformers: PPTX, PDF
||Large language models: PPTX, PDF
||Vision transformers, other trends: PPTX, PDF
||Assignment 5 due May 5|
Extra credit assignment due May 8
Final project reports due May 10
Other deep learning courses with useful materials
Useful textbooks available online
Statement on mental health
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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