CIS 515 Fundamentals of Linear Algebra & Optimization (Math for Machine Learning)

Short Description

There are hardly any machine learning problems whose solutions do not make use of linear algebra. This course places emphasis on linear regression, data compression, support vector machines and more, which will provide a basis for further study in machine learning, computer vision, and data science.

Portfolio Building Course

No

Pre-Requisites

Calculus (Chapters 8, 9, 10, and 48 of Schaum’s Outlines of Calculus fifth edition by Frank Ayers and Elliott Mendelssohn) Suggested: Undergraduate course in linear algebra (helpful but not required), Chapters 1 through 3 of Schaums Outline of Linear Algebra, fourth version by Seymour Lipschitz and Marc Lipson

Content

There are hardly any machine learning problems whose solutions do not make use of linear algebra. This course presents tools from linear algebra and basic optimization that are used to solve various machine learning and computer science problems. It places emphasis on linear regression, data compression, support vector machines and more, which will provide a basis for further study in machine learning, computer vision, and data science. Both theoretical and algorithmic aspects will be discussed, and students will apply theory to real-world situations through MATLAB projects.

Course Offerings
  • Fall 2021 Not Offered
  • Spring 2022 Jean Gallier, Jocelyn Quaintance
  • Summer 2022 Not Offered
Course Creators
  • Jean Gallier
  • Jocelyn Quaintance