This is a previous offering of this course; the most recent offering is Winter 2022

Course Description

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification and object detection. Recent developments in neural network approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of neural-network based deep learning methods for computer vision. During this course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. We will cover learning algorithms, neural network architectures, and practical engineering tricks for training and fine-tuning networks for visual recognition tasks.

Quick Information

Class time: Mondays and Wednesdays, 1:30pm - 3pm
Location: Zoom (meeting link on Canvas)
Syllabus: The syllabus has detailed course policies
Schedule: The schedule has lecture slides and recommended reading
Office Hours: Google Calendar
Discussion forum: Piazza
Fall 2020 Lecture videos (UMich only): Google Drive
Fall 2019 Lecture videos (public): YouTube
Assignments: [A1] [A2] [A3] [A4] [A5] [A6]
Previous Offerings: Fall 2019

Other Information

Korean Translation: Professor Young-Geun Choi has translated our course materials into Korean for a course at Sookmyung Women's University