Grok is amazing, you can just learn things now lol
Listed the rencent (2023-25) and relavant Uni / Grad courses about image understanding, that can be used either as learning materials, or as RAG grounding sources.
Comprehensive Overview of Web-Accessible University Courses on Advanced Image Understanding (2023–2025)
This document provides a carefully curated list of university and graduate-level courses from top-tier institutions that offer openly accessible online materials such as lecture slides, notes, and assignments. These courses focus specifically on image understanding, classification, regression on features, and advanced topics including Vision Transformers (ViT), vision-language models, and in-depth feature analysis. Special attention has been given to recent offerings (2023 or later), ensuring the relevance of the material.
- Courses are selected from leading universities: Stanford, MIT, UC Berkeley, Carnegie Mellon, Princeton, University of Washington, University of Pennsylvania, UCLA, University of Michigan, and UT Austin.
- All courses listed offer web-accessible materials suitable for self-study.
- Emphasis is placed on courses offering advanced content, including ViT and aesthetic analysis.
| Institution | Course Code | Course Title | Key Topics Covered | Materials Availability |
|---|---|---|---|---|
| Stanford University | CS231n | Deep Learning for Computer Vision | ViT, vision-language models, neural networks | GitHub Notes, public lecture videos |
| MIT | 6.S191 | Introduction to Deep Learning | Vision applications, style/aesthetic analysis | MIT OCW |
| UC Berkeley | CS 280A | Computing with Visual Data | Image processing, feature detection, segmentation | Course Page |
| Carnegie Mellon University | 16-385 | Computer Vision | Object recognition, motion analysis, feature extraction | Course Page |
| Princeton University | COS 429 | Computer Vision | 3D vision, multi-view geometry, recognition | Course Page |
| University of Washington | CSE455 | Computer Vision | Feature detection, neural networks | Course Page |
| University of Pennsylvania | CIS 581 | Computer Vision and Computational Photography | Image features, computational photography | Penn Online |
| UCLA | COMSCI163 | Deep Learning for Computer Vision | Neural architectures, vision tasks | Course materials availability needs confirmation (course listed publicly: check UCLA department resources) |
| University of Michigan | EECS 498-007 / 598-005 | Deep Learning for Computer Vision | Algorithms, neural architectures, deep feature analysis | Course Page |
| University of Washington | CSE455 | Computer Vision | Deep networks, feature analysis, advanced vision tasks | Course Page |
| University of Texas at Austin | CS376 | Computer Vision | Regression on image features, machine learning approaches | Course Page |
- Stanford CS231n and MIT 6.S191 are particularly recommended as they extensively cover advanced topics like Vision Transformers (ViT), vision-language integration, and even style/aesthetic analyses.
- UC Berkeley CS 280A and Carnegie Mellon 16-385 provide substantial content on image feature analysis, feature extraction, and regression.
- Princeton COS 429 and University of Pennsylvania CIS 581 offer more focused content on computational aspects of vision and recognition.
- All listed courses include accessible online materials suitable for self-study.
- Courses have been selected based on publicly available materials, aligning with academic standards of top universities.
- Most materials include slides, assignments, notes, and in some cases recorded lectures, facilitating comprehensive learning.
This curated list should provide robust, high-quality educational resources suitable for anyone seeking an in-depth understanding of modern image classification, regression techniques, and advanced image feature analysis from renowned global universities.