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PHY2109 0.25FCE
Special Topics in Physics II- Intro to statistical inference and machine learning

Official description

This course will cover conceptual foundations and practical applications of a number of common statistical learning methods – from classical tools to their modern incarnations, with the emphasis on the connection among the different tools and their relation to a number of models of mathematical physics.

Specific topics will include: maximum likelihood estimation, Bayesean methods, clustering and dimensionality reduction, neural networks and Boltzmann machines, supervised and unsupervised learning, feature extraction and learning.

The mathematical methods will be illustrated by examples from non-equilibrium statistical mechanics, physical chemistry, population dynamics and epidemiology, quantum optics, solid state physics, chemical reactions and gene regulation.

Additional information

TBA, after polling students

course title
PHY2109 0.25FCE
quarter course (0.25 FCE credit)
time and location
Wednesdays 4 - 6 pm, MP1115

Delivery Methods

In Person

A course is considered In Person if it requires attendance at a specific location and time for some or all course activities.*.

* Subject to adjustments imposed by public health requirements for physical distancing.

Online - Synchronous
A course is considered Online Synchronous if online attendance is expected at a specific time for some or all course activities, and attendance at a specific location is not expected for any activities or exams.
A course is considered Asynchronous if it has no requirement for attendance at a specific time or location for any activities or exams.