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Convex Optimization | Machine Learning | Mathematical Programming

Carnegie-Mellon University

Explore the fundamentals of convex optimization, including convexity, optimization basics, and canonical problem forms. Recommended for students interested in machine learning and optimization.

University CoursesMachine Learning

Introduction

Convex Optimization is a course that covers the fundamentals of convex optimization, including convexity, optimization basics, and canonical problem forms. The course is offered at Carnegie Mellon University and is cross-listed as Statistics 36-725.

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Highlights

  • Covers the theoretical foundations of convex optimization
  • Includes lectures, quizzes, and video recordings for each topic
  • Taught by an experienced instructor, Ryan Tibshirani, and a team of TAs
  • Provides opportunities for scribing and class discussions through Piazza

Recommendation

This course is recommended for students interested in machine learning, optimization, and mathematical programming. It provides a solid foundation in convex optimization and is suitable for both graduate and advanced undergraduate students.

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