CS444: Deep Learning for Computer Vision (Fall 2023)Instructor: Saurabh Gupta (saurabhg) To the extent possible, you should use Campuswire for asking questions. If you have a logistics question specific to yourself, you can email the staff at dlcv-fa23-staff@lists.illinois.edu. If you have a question or concern that you only want to share with the instructor, please email at saurabhg@illinois.edu. Course DescriptionThis 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 advanced topics (like NeRFs, self-supervision, vision and language). Coursework will consist of programming assignments in Python. Those registered for 4 credit hours will have to complete a project. PrerequisitesMulti-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. Recommended Textbooks
Grading SchemeTentative and subject to changes in the first few weeks.
Up to 1% extra credit for actively participating in class and on Campuswire. Enrollment
AcknowledgementThis course is largely based on Prof. Svetlana Lazebnik's Deep Learning for Computer Vision course. We would like to thank her and the many researchers who have made their slides and course materials available. |