PHYS 578 B: Selected Topics in Theoretical Physics

Welcome to PHYS 578. My name is Armita Nourmohammad (pronouns she/her), and I will be your instructor. 

In this class everyone is welcome, regardless of other identities you hold in addition to that of physics student.  I expect everyone in this class to treat each other with respect.

Overview

With the growing amount of data ranging from cosmological measurements to biological data and finance, statistical inference has become ubiquitous in different scientific disciplines to learn quantitative models from data and to interpret the structure of these models. A large body of work has shown that statistical inference is tightly connected to concepts from statistical physics and limits of inference could be understood in terms of physical phenomena such as phase transition. In addition, statistical physics of disordered systems have been impactful  in development of algorithms for inference problems, including compressed sensing, machine learning, and generalized linear regression. In this course we explore fundamentals of inference techniques through the lens of statistical physics. Specifically, we will cover topics on information theory, probabilistic inference, optimization, and machine learning, and will discuss applications of these approaches to learning from large datasets. Inspired by these topics, students will work in groups on small projects and will present their work at the end of the quarter.

Getting started

• Slack will be the primary space where you can communicate with me and the rest of the class. To get started with this space, please:
  
  • Use your UW email to join our Slack workspace.
  • Get Slack running on your device. Here are useful tips and features for using Slack. I highly recommend downloading the mobile or desktop app, and turning notifications on so that you don't miss messages.
    
    

Lecture Materials and Assignments

• Go to the Modules tab (on the left) and review each week's notes and recorded videos on the week's material.
• Each week's assignments and the suggested papers can be found in the week's Module
  
  
  

Lectures and Office Hours

• Lectures take place in person on Tue and Thu 11:30 am- 12:50 pm at PAA A114, and we aim to post the recordings of these lectures throughout the quarter. You can directly access from the canvas menu (on the left).
  • The direct link to the lecture zoom meetings:
    https://washington.zoom.us/j/95744318686
  • Recorded lectures can be accessed under the "cloud recording" in the canvas zoom page
• Office hours: TBD

Contact information

For questions either send an email (armita@uw.edu) or a canvas message to me.

Recommended reading:

• Biophysics: Searching for Principles (William Bialek); Princeton University Press, 2012 (early draft of the book)
• Information theory, inference, and learning algorithms (David McKay); Cambridge University
  Press, 2003 (downloadable version). Also check out the YouTube videos of D. McKay.
• Pattern Recognition & Machine Learning (C. Bioshop): Springer, 2016 (downloadable version)
• Information, Physics and Computation (M. Mézard, A. Montanari): Oxford University Press, 2009 (early draft of the book)
• Statistical physics of inference: Thresholds and Algorithms (L. Zdeborová, F. Krzakala); file
• Statistical mechanics of complex neural systems and high dimensional data 
(M. Advani, S. Lahiri, S. Ganguli): file
• Paper repository with links to relevant papers can be found here.
  
  The relevant sections will be noted in the weekly materials.

Credits

To get credits in this class it is expected that you:

• Do a comprehensive presentation of an assigned topic
• Participate in Journal clubs (discussion + questions)
• Participate in class discussion

Access and accommodation

Your experience in this class is important to me, so if you have a temporary health condition or permanent disability that requires accommodations (conditions include but are not limited to: mental health, attention-related, learning, vision, hearing, physical), please contact DRS to arrange accommodations.

Safe campus

I am committed to ensuring a safe environment on campus.  I encourage you to check out the resources available here.

Religious Accommodations

Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy (https://registrar.washington.edu/staffandfaculty/religious-accommodations-policy/). Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form (https://registrar.washington.edu/students/religious-accommodations-request/).

Academic integrity and student conduct

The University takes academic integrity and student conduct very seriously.  Behaving with integrity and respect is part of our responsibility to our shared learning community.  Acts of academic misconduct may include, but are not limited to, cheating by working with others or sharing answers on exams.

Please note that screenshots or recordings of instructors, other students, and course materials during active video (Zoom) participation sessions are strictly forbidden.  Streaming or posting inappropriate materials on any course platform is also not allowed. 

All the course materials including lecture notes, lecture videos are intellectual properties of the instructor and the University of Washington. Distributing them in any form without permission is forbidden.  

The University of Washington Student Conduct Code (WAC 478-121) defines prohibited academic and behavioral conduct and describes how the University holds students accountable as they pursue their academic goals.  Allegations of misconduct by students may be referred to the appropriate campus office for investigation and resolution.  More information can be found online at https://www.washington.edu/studentconduct/.

If you’re uncertain about if something is academic or behavioral misconduct, ask me.  I am willing to discuss questions you might have.