Spring 2023 CS 444 Deep Learning for Computer Vision
Quick links: schedule, assignment submission, quizzes, grades, announcements and discussion, policies, lecture videos
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.
TAs:
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.
Grading scheme:
- Programming assignments: 80% of the grade for 3-credit students and 60% of the grade for 4-credit students
- Can optionally be done in pairs -- but see important disclaimers on group work!
- Will be using PyTorch, Google Colab, and Google Cloud
- 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
Be sure to read the course policies!
Schedule (tentative)
| Date | Topic | Assignments |
| January 18 | Past and present of DL: PPTX, PDF | Self-study: Python/numpy tutorial |
| January 23 | Overview of DL problems: PPTX, PDF | |
| January 25 | Linear classifiers: PPTX, PDF | |
| January 30 | Linear classifiers cont. (slides above) | |
| February 1 | Linear classifiers cont.: PPTX, PDF | Assignment 1 is out |
| February 6 | Nonlinear classifiers: PPTX, PDF | |
| February 8 | Nonlinear classifiers cont. | |
| February 13 | Backpropagation: PPTX, PDF | Assignment 1 due February 14 |
| February 15 | Convolutional networks: PPTX, PDF | Assignment 2 is out |
| February 20 | Convolutional architectures: PPTX, PDF | |
| February 22 | Convolutional architectures cont. | |
| February 27 | Training in detail: PPTX, PDF | |
| March 1 | PyTorch tutorial: Jupyter notebook | |
| March 6 | Object detection: PPTX, PDF | Assignment 2 due March 6 |
| March 8 | Dense prediction: PPTX, PDF | Project proposals due March 10 Assignment 3 is out |
| March 20 | Self-supervised learning: PPTX, PDF | |
| March 22 | Self-supervised learning cont. | |
| March 27 | Adversarial examples: PPTX, PDF | |
| March 29 | Generative adversarial networks: PPTX, PDF | |
| April 3 | Advanced GAN models: PPTX, PDF | Assignment 3 due April 4 |
| April 5 | Advanced GAN models cont. | Assignment 4 is out |
| April 10 | Image-to-image translation: PPTX, PDF | |
| April 12 | Diffusion models: PPTX, PDF | Project progress reports due April 14 |
| April 17 | Deep Q-learning: PPTX, PDF | |
| April 19 | Policy gradient methods: PPTX, PDF | Assignment 4 due April 19 Assignment 5, extra credit assignment are out |
| April 24 | Recurrent networks: PPTX, PDF | |
| April 26 | Models with attention, transformers: PPTX, PDF | |
| May 1 | Large language models: PPTX, PDF | |
| May 3 | Vision transformers, other trends: PPTX, PDF | Assignment 5 due May 5 Extra credit assignment due May 8 Final project reports due May 10 |
Resources
Other deep learning courses with useful materials
- Stanford CS231n: Convolutional Neural Networks for Visual Recognition
- U Michigan EECS 498: Deep Learning for Computer Vision
- MIT 6.S191: Introduction to Deep Learning
- Princeton COS 495: Introduction to Deep Learning
- UT Austin CS 342: Deep Learning
- IDIAP EE559: Deep Learning
- ENS Deep Learning: Do It Yourself
- U of I IE 534: Deep Learning
- MIT Structure and Interpretation of Deep Networks
- Berkeley CS285: Deep Reinforcement Learning
- Full Stack Deep Learning
Tutorials
Useful textbooks available online
- Deep Learning by I. Goodfellow, Y. Bengio and A. Courville
- Dive into Deep Learning -- an interactive online book by A. Zhang, Z. Lipton, M. Li and A. Smola from Amazon
- Michael Nielsen's online book on Neural Networks and Deep Learning
- Hastie, Tibshirani and Friedman, Elements of Statistical Learning
- David Forsyth's Applied Machine Learning textbook draft
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