Combinatorial Algorithms & Data Structures | CS 270, Spring 2021

Explore advanced algorithmic techniques and their applications in machine learning and data analysis. Learn gradient descent, linear algebra, and more in this comprehensive course.

Introduction

This course covers fundamental combinatorial algorithms and data structures, including convex optimization tools, linear algebraic techniques, and their applications in computer science.

Highlights

• Lectures on gradient descent, mirror descent, multiplicative weights, centroid and ellipsoid algorithms, and their applications
• Exploration of linear algebraic tools such as solving linear systems, PCA, SVD, and their uses in Gaussian mixtures and robust mean estimation
• In-depth coverage of tensors, Jenrich's algorithm, and independent component analysis

Recommendation

This course is recommended for students interested in learning advanced algorithmic techniques and their applications in areas like machine learning and data analysis. The course provides a strong foundation in combinatorial optimization and linear algebra, which are essential for many problems in computer science.