AIMS course: Physics for Machine Learning
Information
- Lecturer: Prof. Hugo Touchette
- Lectures: Mon and Tues afternoons
- Tutorials: Thurs afternoons
References
- Basic references on probability theory:
- S. Ross, A First Course in Probability, Prentice-Hall, 2010.
- G. Grimmett and D. Stirzaker, Probability and Random Processes, OUP, 2001.
- K. Jacobs, Stochastic Processes for Physicists, CUP, 2010.
- Main references on MC:
- J. S. Liu, Monte Carlo Strategies in Scientific Computing, Springer, 2001.
- C. A. L. Bailer-Jones, Practical Bayesian Inference, CUP, 2017.
- A. Guyader, Méthodes Monte Carlo, Lecture notes, Sorbonne Université, 2022.
- Others:
- S. Asmussen, P. W. Glynn, Stochastic Simulation, Springer, 2010.
- D. J. C. MacKay, Information Theory, Inference, and Learning Algorithms, CUP, 2003.
- K. P. Murphy, Machine Learning: A Probabilistic Perspective, MIT, 2012. Chaps 23, 24.
Primary notes
From the SU course: Monte Carlo Methods for ML (ML822)
Secondary notes
From the SU course: Applied Markov Processes (AM783)
Courseworks
Demos
Extras
Tutorials
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