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Modern AI course and LLM interpretability course

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### Artificial Intelligence
- [CS50 - Introduction to Artificial Intelligence with Python (and Machine Learning), Harvard OCW](https://cs50.harvard.edu/ai/)
- [10-202: Introduction to Modern AI - CMU](https://modernaicourse.org/)
- [CS 188 - Introduction to Artificial Intelligence, UC Berkeley - Spring 2025, by John Canny, Oliver Grillmeyer](https://inst.eecs.berkeley.edu/~cs188/sp25/) ([Spring 2024](https://inst.eecs.berkeley.edu/~cs188/sp24/)) ([Spring 2023](https://www.youtube.com/playlist?list=PLp8QV47qJEg7WWVg_5eOECzVPpy23UjJz))
- [6.034 Artificial Intelligence, MIT OCW](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/)
- [CS221: Artificial Intelligence: Principles and Techniques - Autumn 2019 - Stanford University](https://www.youtube.com/playlist?list=PLoROMvodv4rO1NB9TD4iUZ3qghGEGtqNX)
@@ -595,6 +596,7 @@ Table of Contents
- [UT Austin - Advances in Deep Learning](https://ut.philkr.net/advances_in_deeplearning/)
- [HKU - Data 8014 Principles of Deep Representation Learning Fall 2025, by Yi Ma](https://ma-lab-berkeley.github.io/deep-representation-learning-book/community.html)
- [Local Explanations for Deep Learning Models (Fall 2023)](https://www.youtube.com/playlist?list=PLbuogVdPnkCoto690B1eTReTznuSC9qYX)
- [CS 6966/5966 Interpretability of LLMs (Spring 2026) - University of Utah](https://utah-llm-interpretability.notion.site/s26)
- #### **Reinforcement Learning**