Labs in the Heidelberg area make leading contributions to the theory and practice of machine learning, pattern recognition and artificial intelligence. Below is a summary of pertinent courses and activities in the area. Please follow the links to learn more about contents and prerequisites. Enjoy!  #
from David Mount !
Alternate Lecture notes at:
- https://www.cs.umd.edu/users/meesh/cmsc351/mount/lectures/
- https://www.cs.umd.edu/~mount/251/Lects/251lects.pdf
This is CMSC389F, the University of Maryland's theoretical introduction to the art of reinforcement learning. An introductory course taught by Kevin Chen and Zack Khan, CMSC389F covers topics including markov decision processes, monte carlo methods, policy gradient methods, exploration, and application towards real environments in broad strokes .
M. Hindmarsh, M. Lüben, J. Lumma, and M. Pauly. (2020)cite arxiv:2008.09136Comment: Introduction to the topic of phase transitions in the early universe, 63 pages.
A. Green. (2021)cite arxiv:2109.05854Comment: 35 pages, 3 figures. Submitted to SciPost Physics Lecture Notes, Les Houches Summer School Series, v2: minor changes.