PHYS 503 :: Physics Illinois :: University of Illinois at Urbana-Champaign

Instrumentation Physics: Applications of Machine Learning

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Instructor: Mark Neubauer

Teaching assistant/grader: Tony Mirasola.

Course Description

This course is designed to give students a solid foundation in machine learning applications to physics, positioning itself at the intersection of machine learning and data-intensive science. This course will introduce students to the fundamentals of analysis and interpretation of scientific data, and applications of machine learning to problems common in laboratory science such as classification and regression. There will be two 75-minute classes each week, split into discussions of core principles and hands-on exercises involving coding and data. There will be a few projects throughout semester that will build on the course material and utilize open source software and open data in physics and related fields. The list of topics will evolve, according to the interests of the class and instructors. Material will be clustered into units of varying duration, as indicated below. The lists of suggested readings and references are advisory; a large amount of material of excellent quality is now available on the worldwide web, particularly on the sites of university courses addressing the topics of each unit.

A distinguishing feature of this course is its sharp focus on endeavors in the data-rich physical sciences as the arenas in which modern machine learning techniques are taught. The course uses open scientific data, open source software from data science and physics-related fields, and publically-available information as enabling elements. Research-inspired projects are an important part of the course and students will not only execute them but will play an active role in helping define and shape them. Example projects might include machine learning approaches to searches for new particles or interactions at high-energy colliders; methods of particle tracking and reconstruction; identification, classification and measurement of astrophysical phenomena; novel approaches to medical imaging and simulation using techniques from physics and machine learning; machine learning in quantum information science. Through these projects and the course material, students will learn how large datasets in physics are generated, curated, and analyzed, using machine learning as a tool to generate key insights in both experimental and theoretical science.

Learning objectives

As a result of completing this course, students will

Meeting Times

Two 75-minute class sessions per week (Monday, Wednesday 1:00 - 2:15) for 14 weeks; optional office hours as necessary/desired.

Credit and grading

Students must register for this course in the fall semester for a total of 4 credit hours. Grading is by letter.

Academic Integrity

All activities in this course, including documentation submitted for petition for an excused absence, are subject to the Academic Integrity rules as described in Article 1, Part 4, Academic Integrity, of the Student Code.