Machine Learning Techniques for Physics

This course will provide an introduction to basic concepts of machine learning techniques used in physics research, as well as a survey of specific techniques and tools and their applications to physical problems. Topics may include: classification, decision trees, dimensionality reduction, neural networks and deep learning, autoencoders, anomaly detection. Familiarity with coding in Python is required. At least one 200-level Physics course is recommended.