![]() 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. 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. 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. Questions? Contact "Placement & Proficiency" staff by emailing or by calling 21 during business hours.PHYS 495 Where the Arts Meets Physics credit: 3 Hours. Students should consult with their academic advisor to determine how this test-based credit may best be used to meet their specific curriculum and program requirements. Students can apply elective credit towards their graduation requirements. This is because our campus does not have an equivalent course that corresponds to the AP course. If you do not see the new policies for 2022-2023, please clear out your cache and refresh your browser.įor descriptions of the courses listed in the table below, please see the Courses of Instruction section in the Academic Catalog.Ĭourse numbers listed as 1 - or 2 - are considered elective credit within the stated subject area. ![]() Only scores resulting in credit are shown. The credit policies below apply to all students entering Illinois in Summer 2022, Fall 2022, or Spring 2023. Please be sure to tell your academic advisor during Summer Registration that you have taken AP exams, as the College Board has announced there will be a delay in sending scores to institutions. The University of Illinois at Urbana-Champaign is committed to supporting our incoming students and ensuring that they have a smooth transition to their higher education studies.
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