Advanced Probabilistic Machine Learning
Spring 2024
Details
Credit
3Mode
In PersonComponent
LectureMeets
Instructors
Jimi OkeDescription
We will study machine learning (ML) via a probabilistic model-based approach informed by Bayesian inference. This provides a structure for developing techniques and systems that can address a wide range of problems relating to: inferring data processes, prediction, generation, discovering latent structures and decision-making. Fundamentals of probability, statistics, graphical modeling, information theory and optimization will also be covered in the early part of the course. Students will gain a deep understanding of the probabilistic approach to ML through lectures, problem sets, two midterms and a final project. Theoretical considerations will be balanced by applications to engineering and scientific problems.
Eligibility
Dates
Start Date
End Date
Subject Details
Subject Description
Subject
Catalog Number
Class Number
Catalog Details
Course ID
Section
Academic Career
Meeting
Annotation
For complete and up-to-date class details, meeting times, and textbook information, search for this class in Spire." Select the class term "Spring 2024 " and subject "Civil & Environmental Engrg " and enter the class number "20412 ".
If the class is not open for enrollment, you may need to specify other search criteria.