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PHYS 248 A: Introductory Selected Topics

Meeting Time: 
TWThF 9:30am - 11:50am
PAA A212
Shih-Chieh Hsu

Syllabus Description:

Course Description:

One of the greatest scientific triumphs in the 21st century is the deep understanding of the fundamental building blocks of matter and their interactions at the zepto scale. The Higgs Boson discovery in 2012 completes the last missing pieces of this theory, known as the Standard Model.

This is not end of the journey, however. Scientists believe that invisible dark matter and dark energy make up 95% of the total energy and mass of the universe. These theories suggest frontiers of physics beyond the Standard Model. The revolution of artificial intelligence (AI), in particular deep learning, offers a brand-new way of thinking to investigate the data and unravel deep mysterious of the universe.

What the Course Covers:

This course introduces you to cutting-edge quantum theory and how it explains some of the biggest puzzles in the universe. We’ll look at how state-of-the-art experiments with the Large Hadron Collider (LHC) can be used as tools to examine these groundbreaking theories and hunt for evidence of dark matter. You’ll get an overview of AI algorithms and how they are applied to the LHC data analysis. You’ll also learn about the latest research efforts of physicists at the UW to advance our understanding of quantum theory.

Course style

The course will be in-person. Before each class, students will study pre-lecture video/documents and complete pre-lecture quiz. The in-person lecture will be extensions based on the pre-lecture materials.

Who Should Attend?

Students interested in the different aspects of quantum theory and how AI can be used to advance discoveries. Knowledge of high school physics is recommended but not required.


There is no official textbook in this class. However, there are two recommended reference materials that I'll follow closely for the lecture.

Grading Policy:

  • Credits: 5
  • Pre-Lecture Quiz (due 10:30 pm the day before the lecture) and in-class participation: 16%
    • The least 2 scores will be excluded from the final grade calculation
    • Allowed attempts: 3
  • Homework (due 11:59 pm Mon) and oral report
    • homework I: 15%
    • homework II: 15%
    • homework III: 15%
    • homework IV: 15%
  • Student Symposium Oral presentation (at least two people in a group)
    • elevator speeches: 8%
    • final report: 16%
  • Late submission penalty  (20% penalty). Exempt of penalty has to be discussed prior to due time.


No Date Topics Pre-Lecture Lecture AI Lecture
1 Aug 23 Introduction video, pdfquiz pptx
2 Aug 24 Dark Matter videopdf, quiz 02 01 Model
3 Aug 25 Dark Energy video, pdf, quiz pptx 02 Neural Network
4 Aug 26 CENPA Tour video: ADMX GravityHe-6 CRES slide: pptx
5 Aug 30 Relativity I video, pptx, quiz pptx 03 Regression
6 Aug 31 Relativity II video, pptx, quiz pptx 04 Classification
7 Sep 1 Quantum Physics I
CERN Virtual Tour 9am
QMvideo, QMppt, CERNvideo, CERNppt, quiz pptx
8 Sep 2 Quantum Physics II video, pptx, quiz pptx 05 Optimization
9 Sep 6 Symposium: Elevator Speeches video, quiz files
10 Sep 7 The Standard Model I video, pptx, quiz pptx 06 Convolution Neural Network
11 Sep 8 Masterclass I videos, files, quiz pptx
12 Sep 9
Masterclass II [video] videos, slide, quiz pptx
13 Sep 13 The Standard Model II
Research Student panel
video, ppt, quiz pptx
07 Recurrent Neural Network
14 Sep 14 Symposium Final1: G01 to G06 quiz pptx
15 Sep 15 Symposium Final2: G07 to G12 quiz pptx
16 Sep 16 Summary quiz pptx

AI Labs (Optional): These are optional practices for students who are interested in programming exercises. These are labs developed for PHYS427. None of these exercises will be included in the final grade calculation.

GE Requirements: 
Natural Sciences (NSc)
Section Type: 
Last updated: 
September 23, 2022 - 11:27pm