PROBABILITY AND INFORMATION THEORY IN MACHINE LEARNING

Prerequisites & course information

Requisites: ( MATH 320 , 340 , 341 , 375 , or M E/COMP SCI/E C E 532 or concur enroll) and ( E C E 331 , STAT/MATH 309 , 431 , STAT 311 , 324 , 424 , MATH 531 , or M E 424 prior to Fall 2026), grad/profsnl standing, or decl in Capstone Cert in Computer Sciences for Professionals Course Designation: Level - Advanced L&S Credit - L&S Liberal Arts and Science Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2024 Learning Outcomes: 1. Identify how ambiguity and noise leads to the need for probabilistic methods in machine learning Audience: Both Grad & Undergrad 2. Implement classification, prediction and generative algorithms using a variety of techniques based in probability, information theory and machine learning Audience: Both Grad & Undergrad 3. Prove optimality of a variety of algorithms and demonstrate understanding of sample complexity bounds Audience: Graduate Learning Outcomes: 1. Identify how ambiguity and noise leads to the need for probabilistic methods in machine learning Audience: Both Grad & Undergrad 2. Implement classification, prediction and generative algorithms using a variety of techniques based in probability, information theory and machine learning Audience: Both Grad & Undergrad 3. Prove optimality of a variety of algorithms and demonstrate understanding of sample complexity bounds Audience: Graduate