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. 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.
#UIUC PHYSICS 101 LAB WEEK 1 SOFTWARE#
The course uses open scientific data, open source software from data science and physics-related fields, and publicly-available information as enabling elements. 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 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. Material will be clustered into units of varying duration, as indicated below. The list of topics will evolve, according to the interests of the class and instructors. 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. There will be two 75-minute classes each week, split into discussions of core principles and hands-on exercises involving coding and data. 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. PHYS 503 Instrumentation Physics Applications of Machine Learning credit: 4 Hours.ĭesigned to give students a solid foundation in machine learning applications to physics, positioning itself at the intersection of machine learning and data-intensive science. Prerequisite: Instructor Approval Required. The event will be specific to each offering and may include activities such as physics-based museum exhibits and performance pieces.
The projects will be presented at a culminating event at the end of the semester. This process will include: Project design independent study team work and dedicated assignments. With collaboration and guidance from their instructors and across-campus experts, student projects will be taken from inception to completion. Identifying themes based on their exposure and interest, students will form interdisciplinary project teams. Students will explore the stunning creations that have emerged from synergies between the sciences and the arts. Students will explore such physics topics while they actively participate in a broad range of artistic practices and expression.
Where Art Meets Physics is a project-based, cross-disciplinary course for students interested in both exposure to the frontiers of physics and experiences in the arts.
PHYS 495 Where the Arts Meets Physics credit: 3 Hours.
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