Zebang Shen: Catalogue data in Spring Semester 2024 |
| Name | Dr. Zebang Shen |
| Address | Institut für Maschinelles Lernen ETH Zürich, OAT Y 21.2 Andreasstrasse 5 8092 Zürich SWITZERLAND |
| zebang.shen@inf.ethz.ch | |
| Department | Computer Science |
| Relationship | Lecturer |
| Number | Title | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 252-5256-00L | AI for Science Seminar  The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | 2 credits | 2S | N. He, Z. Shen | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Artificial intelligence (AI) and machine learning (ML) offer significant potential to revolutionize the fundamentals of scientific computation and discovery today. The goal of this seminar course is to expose student to the recent development of "AI for Science". | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The aim of this course is to showcase how AI techniques, such as deep learning, can enhance scientific research in the field of Physics. Students will first learn about relevant scientific models, such as key Partial Differential Equations and their associated dynamical systems. They will also explore various AI methods designed to advance traditional approaches. Furthermore, we will guide students through the actual implementation of foundational algorithms, enabling them to address critical scientific issues hands-on. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | 1. Introduction to related scientific models. 2. AI methods designed to address the scientific problem. 3. Implementation of some fundamental algorithms. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | The related papers will be released in the first session of the seminar. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Basic knowledge of multivariate calculus, linear algebra, probablilty theory. The student is assumed to be familiar with Python. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Competencies |
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