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Boltzmann Law: Physics to Machine Learning

ECE59500

Credit Hours:

1

Start Date:

March 29, 2021

Learning Objective:

  • Explain the law of equilibrium and entropy
  • Understand the operation of Boltzmann machines
  • Understand the operation of quantum circuits

Description:

This course is intended to be broadly accessible to students in any branch of science or engineering who would like to learn about the conceptual framework for equilibrium statistical mechanics and its application to modern machine learning.

Weekly topics: 1) Boltzmann Law; 2) Boltzmann Machines; 3) Transition Matrix; 4) Quantum Boltzmann Law; 5) Quantum Transition Matrix
Spring 2021 Syllabus

Topics Covered:

Key concepts of statistical mechanics, self-consistent field method for interacting systems, Boltzmann machines

Prerequisites:

This course is designed for students who have an undergraduate degree in engineering or the physical sciences, having a familiarity with differential equations and linear algebra.

Applied / Theory:

10 / 90

Projects:

None

Exams:

Three exams

Textbooks:

None

Computer Requirements:

ProEd Minimum Requirements:

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Tuition & Fees:

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