Syllabus#

Course Description#

The course “Hands-on Artificial Intelligence (AI) for Physics” is a project-based upper division course aimed to provide practical skills to perform research with neural networks. We will survey the fundamentals of learning in Artificial Neural Networks (ANN) and describe the underlying principles making neural networks generic computing frameworks. We will then build computational skills for training different types of neural networks, such as Convolutional networks, Recurrent Neural Network, Generative Neural Network. Students will learn how to apply the state-of-the-art AI and data science tools on cutting-edge research problems in interdisciplinary fields in physics including particle physics, condensed matter physics and astronomy.

Classroom Format#

  • Experience-based learning:

    • Work out a set of in-class exercise during lectures

  • Post-lecture Lab:

    • Carry out hands-on assignments (homework)

  • Final project

    • Apply machine learning techniques on data provided by instructors

  • For in-class exercise and labs: work in groups of three assigned by instructors; rotate every week.

    • Submit one homework for three people

  • For final project: find your own teammate (2 people/team).

    • At least two teams will be working on the same project

    • A kaggle competition will be setup for each project

    • An oral presentation (in English) for each team. Both members have to speak.

Logistics#

  • Course: 11120PHYS591000, National Tsing hua University

  • Lecture Time: 10:10-11:00 Thu

  • In-class exercise/Lab Time: 11:10-13:00 Thu

  • Location: Phys Building Room 208

  • Total People: 24

  • Credit: 3 credits, graded

Requirements#

  • Basic Computer Science and Programming Skills (Python preferred)

  • Linear Algebra

  • Basic Machine Learning and Statistical concepts (optional)

Grading#

  • Class Participation - 10%

  • Lab/Homework - 60%

    • 10-12 sets of assignments

    • Due: Noon on Saturdays (No late submissions)

  • Final Project - 30%

    • Oral presentation: 15%

    • Final project results: 15%

  • Note: 10 points (10% of final grades) off for absence without permission for the following events

    • Final project discussions (Week 16 05/31)

    • Final project oral presentations (Week 17 06/07)