Spring 2022 CS 444 Deep Learning for Computer Vision
Quick links: schedule, assignment submission, quizzes, grades, announcements and discussion, 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.
TAs:
Shivansh Patel (sp58), Junting Wang (junting3), Albert Zhai (azhai2), Zitong Zhan (zitongz3)
Instructor and TA office hours: TBA (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 19 | Introduction: PPTX, PDF | Self-study: Python/numpy tutorial |
| January 24 | Introduction cont.: PPTX, PDF | |
| January 26 | Linear classifiers: PPTX, PDF | |
| January 31 | Linear classifiers continued | |
| February 2 | Linear classifiers continued: PPTX, PDF | Assignment 1 out |
| February 7 | Nonlinear classifiers: PPTX, PDF | |
| February 9 | Nonlinear classifiers continued | |
| February 14 | Backpropagation: PPTX, PDF | Assignment 1 due February 15 |
| February 16 | Convolutional networks: PPTX, PDF | Assignment 2 out |
| February 21 | Convolutional networks cont. | |
| February 23 | Convolutional networks concluded | |
| February 28 | Training in detail: PPTX, PDF | Assignment 2 due March 1 |
| March 2 | PyTorch tutorial: Jupyter notebook | Assignment 3 Part 1 out |
| March 7 | Object detection: PPTX, PDF | |
| March 9 | Detection cont. | Assignment 3 Part 2 out Project proposals due March 11 (for 4 credits) |
| March 21 | Dense prediction: PPTX, PDF | |
| March 23 | Self-supervised learning: PPTX, PDF | |
| March 28 | Self-supervised learning cont. | |
| March 30 | Generative adversarial networks: PPTX, PDF | |
| April 4 | GAN architectures, trends: PPTX, PDF | Assignment 3 due April 5 |
| April 6 | Other neural generative models: PPTX, PDF | Assignment 4 out |
| April 11 | Recurrent networks: PPTX, PDF | Project progress reports due |
| April 13 | Sequence-to-sequence models with attention: PPTX, PDF | |
| April 18 | Transformers: PPTX, PDF | Assignment 4 due April 19 |
| April 20 | Deep Q-learning: PPTX, PDF | Assignment 5, extra credit assignment are out |
| April 25 | Policy gradient methods: PPTX, PDF | |
| April 27 | Deep RL applications: PPTX, PDF | |
| May 2 | Deep learning trends: PPTX, PDF | |
| May 4 | Societal impacts and ethics: PPTX, PDF | Assignment 5 due May 4 Extra credit assignment, final project reports due May 9 |
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