Spring 2024 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; 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: T TH, 11:00AM-12:15PM
2079 Natural History Building
Lectures will be recorded for later asynchronous viewing. Access to recordings will be restricted to students logged into the illinois.edu domain.

TAs: Shivansh Patel (sp58), James Soole (soole2), Al Smith (ads10), Saharsh Barve (ssbarve2), Kulbir Ahluwalia (ksa5), Mateus Valverde Gasparino (mvalve2)

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
  • Quizzes: 20% of the grade for 3-credit students or 15% for 4-credit students
    • Three 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 16 Introduction: PPTX, PDF Self-study: Python/numpy tutorial
January 18 Introduction cont. (slides above)  
January 23 Linear classifiers: PPTX, PDF  
January 25 Linear classifiers cont.  
January 30 Linear classifiers cont.: PPTX, PDF  
February 1 Linear classifiers cont. Assignment 1 is out
February 6 Multi-layer networks: PPTX, PDF  
February 8 Backpropagation: PPTX, PDF  
February 13 Backpropagation cont.  
February 15 Convolutional networks: PPTX, PDF Assignment 1 due
February 20 Convolutional architectures: PPTX, PDF Assignment 2 is out
February 22 Convolutional architectures cont.  
February 27 Training in detail: PPTX, PDF  
February 29 PyTorch tutorial: Jupyter notebook  
March 5 Object detection: PPTX, PDF Assignment 2 due March 6
March 7 Object detection cont. Assignment 3 is out
Project proposals due March 8
March 19 Object detection cont.  
March 21 Dense prediction: PPTX, PDF  
March 26 Generative adversarial networks: PPTX, PDF  
March 28 Advanced GAN architectures: PPTX, PDF
Image-to-image translation: PPTX, PDF
April 2 Diffusion models (guest lecture by Viraj Shah): PPTX, PDF
Assignment 3 due April 3
April 4 Diffusion models cont. (guest lecture by Viraj Shah): PPTX, PDF
Assignment 4 is out
April 9 Deep Q-learning: PPTX, PDF
April 11 Policy gradient methods: PPTX, PDF
Project progress updates due April 12
April 16 Recurrent networks Assignment 4 due April 17
April 18 Models with attention, transformers Assignment 5
April 23 Large language models  
April 25 Vision transformers, self-supervised learning  
April 30 Trends  
    Final project reports due May 7


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