UMich EECS 498-007 / 598-005: Deep Learning for Computer Vision

Course Website

• Course Website

Course Overview

Actually it was the second course I took about the deep learning. The first one I took is the Machine Learning 2022(HUNG-YI LEE)NTU, also a great course to enter the deep learning world. Compared to that course, this one focuses more on the computer vision part of deep learning. Inheriting from the CS231n course of Stanford University, this course remakes the slides and assignments. And I have to say that the assignments and its tutorials are much better than CS231n, despite some package in Python are a bit outdated and not suitable for the class assignments.

I took this course just by the slides and assignments without watching the lecture videos. The slides are very well made and easy to follow. It’s not just a great course for learning deep learning for computer vision, but also a good resource for reviewing the fundamental concepts of deep learning.

My Learning Journey

• 2025.8.22 Lecture 1 Course Introduction
• 2025.8.23 Lecture 2 Image Classification
• 2025.8.24 Lecture 3 Linear Classifiers
• 2025.8.26 Lecture 4 Regularization + Optimization
• 2025.8.27 Lecture 5 Neural Networks
• 2025.8.28 Lecture 6 Backpropagation
• 2025.9.1 Lecture 7 Convolutional Networks
• 2025.9.3 Lecture 8 CNN Architectures I
• 2025.9.7 Lecture 9 Training Neural Networks I
• 2025.9.8 Lecture 10 Training Neural Networks II
• 2025.9.11 Lecture 11 CNN Architectures II
• 2025.9.12 Lecture 12 Deep Learning Software
• 2025.9.19 Lecture 13 Object Detection
• 2025.9.25 Lecture 14 Object Detectors
• 2025.9.25 Lecture 15 Image Segmentation
• 2025.9.28 Lecture 16 Recurrent Networks
• 2025.10.3 Lecture 17 Attention
• 2025.10.4 Lecture 18 Vision Transformers
• 2025.10.5 Lecture 19 Generative Models I
• 2025.10.8 Lecture 20 Generative Models II
• 2025.10.9 Lecture 21 Visualizing Models and Generating Images
• 2025.10.10 Lecture 22 Self-Supervised Learning
• 2025.10.13 Lecture 23 3D vision
• 2025.10.15 Lecture 24 Videos
• 2025.10.15 Lecture 25 Conclusion
• 2025.8.26 Assignment 1
• 2025.8.31 Assignment 2
• 2025.9.10 Assignment 3
• 2025.10.8 Assignment 4
• 2025.10.13 Assignment 5
• 2025.10.15 Assignment 6