Full Course Syllabus

Mandatory Tutorials (Target Date: Feb 11)

Students should complete the following finetuning tutorials by Feb 11:

  1. Torch-tune: PyTorch Native Finetuning - https://pytorch.org/blog/torchtune-fine-tune-llms/
  2. Unsloth AI: Faster/Memory-Efficient Finetuning - https://docs.unsloth.ai/get-started/fine-tuning-llms-guide

Free Text Books

  1. Multi-Agent Reinforcement Learning Book by Stefano Albrecht, 2024 https://www.marl-book.com/download/marl-book.pdf
  2. Reinforcement Learning by Dimitri P. Bertsekas, 2025 and 2019 (including video lectures) https://web.mit.edu/dimitrib/www/RLbook.html + https://web.mit.edu/dimitrib/www/RLbook.html
  3. Rollout, Policy Iteration, and Distributed Reinforcement Learning by Dimitri P. Bertsekas, 2020 https://web.mit.edu/dimitrib/www/dp_rollout_book.html + https://web.mit.edu/dimitrib/www/Rollout_Complete%20Book.pdf
  4. Parallel and Distributed Computation: Numerical Methods by Dimitri P. Bertsekas and John N. Tsitsiklis, 2018 https://web.mit.edu/dimitrib/www/pdc.html http://www.athenasc.com/pdcbook.pdf
  5. Deep Learning by Ian Goodfellow, 2012 https://www.deeplearningbook.org/
  6. An Introduction to Multi-Agent Systems by Michael Wooldridge, 2001 https://uranos.ch/research/references/Wooldridge_2001/TLTK.pdf
  7. 6G Flagship Book, 2023 https://www.6gflagship.com/news/unveiling-the-digital-horizon-new-book-on-5g-6g-and-future-digital-services-released/
  8. Selected open-source research papers will be provided by the Professor

Open-Access JAX Learning Resources

  1. JAX 101 Tutorial https://docs.jax.dev/en/latest/jax-101.html
  2. JAX Tutorials (including JAX 201 and JAX 301) https://docs.jax.dev/en/latest/_tutorials/index.html
  3. JAX Documentation https://docs.jax.dev/en/latest/jax.html?spm=a2c6h.13046898.publish-article.21.6f9f6ffaIymbyj
  4. JAXMARL https://github.com/FLAIROx/JaxMARL

Open-Source Coding Resources

  1. HuggingFace Transformers coding library https://huggingface.co/docs/transformers/en/index
  2. PyTorch Lightning
    1. Lightning https://lightning.ai/docs/pytorch/stable/
    2. Lightning’s github repository https://github.com/Lightning-AI/pytorch-lightning
    3. Pypi package https://pypi.org/project/pytorch-lightning/
  3. Gymnasium coding library
    1. Gymnasium on Farama https://gymnasium.farama.org/index.html
    2. Farama’s github repository https://github.com/Farama-Foundation/Gymnasium
  4. Python multiprocessing coding library https://docs.python.org/3/library/multiprocessing.html
  5. HuggingFace Deep Reinforcement Learning Materials https://huggingface.co/learn/deep-rl-course/en/unit0/introduction Github: https://github.com/huggingface/deep-rl-class
  6. HuggingFace AI Agents Materials https://huggingface.co/learn/agents-course/en/unit0/introduction Github: https://github.com/huggingface/agents-course

Open-source Unit Testing Resources

  1. Python unittest package https://docs.python.org/3/library/unittest.html
  2. Python pytest package https://pytest.org/

Background

  1. Probability: Introduction to Probability for Computing by Mor Harchol Balter, 2024 https://www.cs.cmu.edu/~harchol/Probability/chapters/HarcholBalterWholeBook.pdf
  2. Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong, 2020 https://mml-book.github.io/book/mml-book.pdf
  3. Convex Optimization for Statistics and Machine Learning, Volume 1: Analysis by Ryan Tibshirani 2025 https://github.com/ryantibs/convexopt-book1/blob/main/book1.pdf
  4. An Introduction to Statistical Learning with Python by Garreth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, 2023 https://www.statlearning.com/