AI Resources¶
These resources were curated by students who took the class.
Acknowledgements
Felipe Martins
Brandon Odegard
Jorge E Gil
Cheryl Roberts
Udacity¶
Udacity AI Nanodegree - [$800 / term (2 terms)] - https://www.udacity.com/course/artificial-intelligence-nanodegree–nd889
Udacity Intro to AI - https://www.udacity.com/course/intro-to-artificial-intelligence–cs271
Udacity - GT Knowledge-Based AI - [Free] - https://www.udacity.com/course/knowledge-based-ai-cognitive-systems–ud409
Udacity - GT - AI for Robotics - [Free] - https://www.udacity.com/course/artificial-intelligence-for-robotics–cs373
Udacity - GT - Reinforcement Learning - [Free] - https://www.udacity.com/course/reinforcement-learning–ud600
Udacity - GT - Machine Learning - [Free] - https://www.udacity.com/course/machine-learning–ud262
GitHub Resources¶
Curated Awesome AI - https://github.com/owainlewis/awesome-artificial-intelligence
Curated Awesome Python - https://github.com/vinta/awesome-python
Curated Awesome ML - https://github.com/josephmisiti/awesome-machine-learning
Coursera¶
Machine Learning by Andrew Ng - https://www.coursera.org/learn/machine-learning
MIT OCW¶
MIT Artificial Intelligence - https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/
Berkeley¶
The AI videos/slides at Berkeley are very good, especially on the topic of Reinforcement learning. Look for the lectures on MDPs and RL and test your knowledge with the “pacman” agents and the robot crawler (my favorite):
http://ai.berkeley.edu/home.html
Also, here are some useful materials for Deep Learning at Stanford. I highly recommend the first sections in Module 1 where they compare k-NN to Neural Networks and all of Module 3 on Convolutional Networks. The NLP content I have less specific pointers in at the moment.
Convolutional Neural Networks: http://cs231n.github.io/
Natural Language Processing: http://cs224d.stanford.edu/syllabus.html