Introduction to AI-Driven High Energy Physics
 

S. Ansari Fard
M. H. Jalali Kanafi

Syllabus:

1) Statistical Learning (Modern Differential Programming, Regression, k-NN, Decision Trees, SVM, K-Means, ect.)

2) Deep learning ( NN, CNN, RNN, GNN, MDN, ect.)

3) Model Inference and Probabilistic Methods

4) Foundational Models (Reinforcement Learning, GAN, Transformers, ect.)

**The programming language is Python, with a focus on PyTorch

Prerequisites:

 1) Introduction to cosmology

 2) Introduction to particle physics

 3) Familiar with basic Python programming

Resources:
[1] T. Hastie, R. Tibshirani, J. Friedman, “The Elements of Statistical Learning, Data Mining, Inference, and Prediction”,  Second Edition, Springer New York, NY  (2009)

[2] I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, MIT Press (2016)

[3] Bishop, Christopher M., and Nasser M. Nasrabadi. “Pattern recognition and machine learning”. New York: springer (2006)

[4] P. Mehta, et. al, “A high-bias, low-variance introduction to Machine Learning for physicists”, Phys. Rept. 810, 1-124 (2019)

[5] G. Carleo, et. al, “Machine learning and the physical sciences”, Rev. Mod. Phys. 91, no.4, 045002 (2019)

[6] https://github.com/iml-wg/HEPML-LivingReview

[7] https://github.com/georgestein/ml-in-cosmology

[8] https://github.com/microsoft/PhiCookBook

[9] J. Alammar and M. Grootendorst, Hands-On Large Language Models, https://github.com/HandsOnLLM/Hands-On-Large-Language-Models, (2024)