The course will take place in the second half of the spring semester 2020 on Tuesdays (7.4 and 21.4 - 26.5) from 14:15 to 16:00 via Zoom due to the COVID-19 pandemic.

Lecturer

Dr. Lewis Tunstall

Abstract

Machine learning is a subfield of artificial intelligence that focuses on using algorithms to parse data, learn from it, and then make predictions about something in the world. In the last decade, this framework has led to significant advances in computer vision, natural language processing, and reinforcement learning. More recently, machine learning has begun to attract interest in the physical sciences and is rapidly becoming an important part of the physicist's toolkit, especially in data-rich fields like high-energy particle physics and cosmology. This course provides students with a hands-on introduction to the methods of machine learning, with an emphasis on applying these methods to solve physics problems. The first half of the course will focus on random forests and gradient boosting, which are perhaps the most widely applicable machine learning models. The second half of the course will cover topological machine learning, which is a relatively new field that combines elements from algebraic topology with statistical learning to capture patterns from high-dimensional data.

Prerequisites

Some basic familiarity with programming languages is assumed.

The course website can be found here.