LINEAR RANDOMIZED ALGORITHMS FOR DATA SCIENCE

Prerequisites & course information

Requisites: Consent of instructor Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2025 Learning Outcomes: 1. Apply common (constructive and inductive) thinking and reasoning patterns used in randomized algorithms for deriving solutions to linear problems arising in data science Audience: Graduate 2. Transform constructive mathematical reasoning patterns into numerical algorithms, and criticize the computational resource utilization of these algorithms Audience: Graduate 3. Implement numerical algorithms using the Julia programming language, and become familiar with assistive tools such as debuggers and profilers Audience: Graduate 4. Create and implement rigorous numerical experiments to test and compare different algorithms and implementations, and draw appropriate conclusions from the results of these experiments Audience: Graduate Learning Outcomes: 1. Apply common (constructive and inductive) thinking and reasoning patterns used in randomized algorithms for deriving solutions to linear problems arising in data science Audience: Graduate 2. Transform constructive mathematical reasoning patterns into numerical algorithms, and criticize the computational resource utilization of these algorithms Audience: Graduate 3. Implement numerical algorithms using the Julia programming language, and become familiar with assistive tools such as debuggers and profilers Audience: Graduate 4. Create and implement rigorous numerical experiments to test and compare different algorithms and implementations, and draw appropriate conclusions from the results of these experiments Audience: Graduate