Spring 2022 CS 444 Deep Learning for Computer Vision

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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
  • 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


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