Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/seokg/awesome-courses
computer graphics / computer vision / machine learning courses
https://github.com/seokg/awesome-courses
List: awesome-courses
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computer graphics / computer vision / machine learning courses
- Host: GitHub
- URL: https://github.com/seokg/awesome-courses
- Owner: seokg
- License: mit
- Created: 2018-04-15T14:05:56.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-09-15T02:01:11.000Z (about 4 years ago)
- Last Synced: 2024-05-22T21:07:38.887Z (4 months ago)
- Homepage:
- Size: 37.1 KB
- Stars: 7
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- ultimate-awesome - awesome-courses - Computer graphics / computer vision / machine learning courses. (Other Lists / PowerShell Lists)
README
# Awesome Courses
computer graphics, computer vision, and machine learning courses
## Computational Photography
* `CS178` Stanford Computational Photography (Marc Levoy) [[link]](https://sites.google.com/site/marclevoylectures/) [[link2]](http://graphics.stanford.edu/courses/cs178-09/)
* CMU Computational Photography [[link]](http://graphics.cs.cmu.edu/courses/15-463/2007_fall/][[link]][http://graphics.cs.cmu.edu/courses/15-463/2010_spring/)
* `CS510` Portland Computational Photography [[link]](http://web.cecs.pdx.edu/~fliu/courses/cs510/index.htm)
## Computer Graphics
* CMU 15-462/662 Computer Graphics [[video]](https://www.youtube.com/watch?v=W6yEALqsD7k&list=PL9_jI1bdZmz2emSh0UQ5iOdT2xRHFHL7E)
* Stanford all Graphics courses [[link]](https://graphics.stanford.edu/courses/)
* `CS348K` Stanford [[link]](http://graphics.stanford.edu/courses/cs348v-18-winter/)
* Scratchapixel 2.0 [[link]](https://www.scratchapixel.com/index.php?redirect)
* Cat like Coding [[link]](https://catlikecoding.com)
## Computer Vision
* `CS131` Stanford Computer Vision: Foundations and Applications [[link]](http://vision.stanford.edu/teaching/cs131_fall1718/index.html)[[link2]](http://cs131.stanford.edu)
* `CS231A` Stanford Computer Vision, From 3D Reconstruction to Recognition [[link]](https://web.stanford.edu/class/cs231a)
* `CS231M` Stanford Mobile Computer Vision [[link]](https://web.stanford.edu/class/cs231m/)
* `CS205A` Mathematical Methods for Robotics, Vision, and Graphics [[link]](https://graphics.stanford.edu/courses/cs205a-13-fall/schedule.html)[[code]](https://www.cs.toronto.edu/~duvenaud/courses/csc2541/index.html)
* `CS664` Cornell Computer Vision [[link]](http://www.cs.cornell.edu/courses/cs664/2008sp/)
* Oxford Vision [[link]](http://www.robots.ox.ac.uk/~az/lectures/index.html)
* `CAP5415` UCF Computer Vision [[link]](http://www.cs.ucf.edu/~mtappen/cap5415/)
* ETH 3D Vision [[link]](http://www.cvg.ethz.ch/teaching/3dvision/courseSchedule.php)
* UW [[link]](https://pjreddie.com/courses/computer-vision/)
* UW 2009wi [[link]](https://courses.cs.washington.edu/courses/cse455/09wi/)
* `CS410` Portland Computer Vision [[link]](http://web.cecs.pdx.edu/~fliu/courses/cs410/index.htm)
* HDU Multi-View Geometry [[link]](https://www.youtube.com/playlist?list=PLoJdZ7VvEiRNQwM3pcwHWwLQutIYMs4KK&fbclid=IwAR3sVumTxv2lWyksGql_KU6ZlwdjhpvtvYAetJkJvQ9CNZO96YghRVK6zvw)
* `KCCV2019` presentation [[link]](https://drive.google.com/drive/folders/1_oFtWc3gWO0blv3CuvwkKX3IVyYIZacf?fbclid=IwAR2wNicqj96Ai9r7HK__I205C0Mj-9FZMgjtBFCgVmxO4lbpzyZxjXvuFHo)
## Deep Learning
* `CS231n` Stanford CNN [[link]](http://cs231n.stanford.edu/)
* `CS294` Stanford Unsupervised Learning [[link]](https://sites.google.com/view/berkeley-cs294-158-sp19/home)
* `CS236` Stanford Deep Generative Models [[link]](https://deepgenerativemodels.github.io/)
* `CS224W` Stanford Machine Learning with Graphs [[link]](http://web.stanford.edu/class/cs224w/)
* Stanford Deep Multi-Task and Meta Learning [[link]](https://www.youtube.com/playlist?list=PLoROMvodv4rMC6zfYmnD7UG3LVvwaITY5&fbclid=IwAR1uNWlGfrjN-OCea3UPMeNB7XTTGpCPCJdKJBm1WfvBACZ9VAciXfvdbW4)
* Stanford all AI courses [[link]](http://ai.stanford.edu/courses/)
* `CS2541` Toronto Differentiable Inference and Generative Model [[link]](https://www.cs.toronto.edu/~duvenaud/courses/csc2541/index.html)
* EPFL Deep Learning [[link]](https://documents.epfl.ch/users/f/fl/fleuret/www/dlc/)
* University Amsterdam [[link]](https://mlvu.github.io)
* DGIST [[link]](https://github.com/InfolabAI/DeepLearning)
## Machine Learning
* UC Berkeley AI [[link]](http://ai.berkeley.edu/home.html)
* `CS205L` Stanford Continuous Mathematical Methods with an Emphasis on Machine Learning [[link]](http://web.stanford.edu/class/cs205l/)
* `CS228` Stanford Probabilistic Graphical Models: Principles and Techniques [[link]](https://cs228.stanford.edu/)
* UBC Machine Learning [[link]](https://www.youtube.com/playlist?list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6)
## Workshop
* GNN [[link]](http://cse.msu.edu/~mayao4/tutorials/aaai2020/?fbclid=IwAR11OVtkSjXKFtA06St2c6wZxQGmXJN2CfYdyoYSuWxmo7SSFfdh5k38dd8)
* CreativeAI [[link]](https://geometry.cs.ucl.ac.uk/creativeai/)
* Computer Vision for Fashion, Art and Design [[eccv2018]](https://sites.google.com/view/eccvfashion) [[iccv2019]](https://sites.google.com/view/cvcreative/)
## ETC
* `EE263` Stanford Introduction to Linear Dynamical Systems [[link]](http://ee263.stanford.edu)
* Immesive Linear Algebra [[link]](http://immersivemath.com/ila/index.html)
* CMU Human Motion Modeling and Analysis [[link]](http://www.cs.cmu.edu/~yaser/Fall2012_15869.html)
* `CS294` Stanford Applied Linear Algebra [[link]](https://sites.google.com/view/berkeley-cs294-158-sp19/home)
* `EE267` Stanford VR [[link]](https://stanford.edu/class/ee267/syllabus.html)
* `STAT479` Univ. Wisconsin-Madison STATS [[link]](https://github.com/rasbt/stat479-machine-learning-fs19?fbclid=IwAR2enpn5S9o2mwqL0_dpgC1cSmRmTaSP-QSGA5kO5AIrWY4kDUkXhH1YPUw)
* 10 Free Online Courses on ML [[link]](https://twitter.com/chipro/status/1157772112876060672?fbclid=IwAR1p_hMoxuPfq_L7z4F5_XDavCo1QDE68Iop8ge8WG2l-YwRoavmoGSmpQ4)