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Learning in Graphical Models | UC Irvine CS 274B | Erik Sudderth

Erik Sudderth

Comprehensive course on advanced topics in graphical models, including probabilistic inference, parameter learning, and structure learning. Taught by expert Erik Sudderth.

University CoursesMachine Learning

Introduction

This course covers advanced topics in graphical models, including probabilistic inference, parameter learning, and structure learning. It is taught by Erik Sudderth at UC Irvine.

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Highlights

  • Covers a wide range of topics in graphical models, including probabilistic inference, parameter learning, and structure learning
  • Taught by an expert in the field, Erik Sudderth
  • Includes video lectures from the Spring 2021 offering of the course

Recommendation

This course is recommended for students and researchers interested in advanced topics in machine learning and graphical models. It provides a comprehensive overview of the field and is taught by a leading expert in the area.

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