Spring 2025 CS 444 Deep Learning for Computer Vision

Quick links: schedule, policies, lecture videos, assignment submission, quizzes, grades, announcements and discussion (code 0068)



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: Kulbir Ahluwalia (ksa5), Rex Cheng (hokeikc2), Ayush Sarkar (ayushs2), Ashutosh Sharma (sharma96), Zhenggang Tang (zt15), Zirui Wang (ziruiw3)

Instructor and TA office hours: See Campuswire (and always check announcements for any last-minute 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 21 Introduction: PPTX, PDF Self-study: Python/numpy tutorial
January 23 Introduction cont. (slides above)  
January 28 Linear classifiers: PPTX, PDF  
January 30 Linear classifiers cont.  
February 4 Linear classifiers cont.: PPTX, PDF  
February 6 Linear classifiers cont. Assignment 1 is out
February 11 Multi-layer networks: PPTX, PDF  
February 13 Multi-layer networks cont.  
February 18 Backpropagation: PPTX, PDF  
February 20 Backpropagation cont. Assignment 1 due
February 25 Convolutional networks: PPTX, PDF Assignment 2 is out
February 27 Convolutional architectures: PPTX, PDF  
March 4 Convolutional architectures cont.  
March 6 Dense prediction: PPTX, PDF  
March 11 Training in detail: PPTX, PDF Assignment 2 due March 12
March 13 PyTorch tutorial: Jupyter notebook Assignment 3 is out
Project proposals due March 14
March 25 Object detection: PPTX, PDF  
March 27 Object detection cont.  
April 1 Object detection cont. Assignment 3 due April 2
April 3 Recurrent networks Assignment 4 out
April 8 Transformers  
April 10 Transformers cont.  
April 15 Deep generative models Project progress updates due
April 17 Deep generative models cont. Assignment 4 due
April 22 Deep generative models cont. Assignment 5 out
April 24 Deep reinforcement learning  
April 29 Deep reinforcement learning cont.  
May 1 TBA  
May 6 TBA Assignment 5 due
Final project reports due TBA

Resources

Other deep learning courses with useful materials

Tutorials

Useful textbooks available online