EE8591
EE 8591 - Predictive Learning from Data (3 Cr.) Online may be available
Electrical and Computer Engineering (11122)
TIOT - College of Science and Engineering
EE 8591 - Predictive Learning from Data (3 Cr.) Online may be available
Course description
Methods for estimating dependencies from data have been traditionally explored in such diverse fields as: statistics (multivariate regression and classification), engineering (pattern recognition, system identification), computer science (artificial intelligence, machine learning, data mining) and bioinformatics. Recent interest in learning methods is triggered by the widespread use of digital technology and availability of data. Unfortunately, developments in each field are seldom related to other fields. This course is concerned with estimation of predictive data-analytic models that are estimated using past data, but are used for prediction or decision making with new data. This course will first present general conceptual framework for learning predictive models from data, using Vapnik-Chervonenkis (VC) theoretical framework, and then discuss various methods developed in statistics, pattern recognition and machine learning. Course descriptions will emphasize methodological aspects of machine learning, rather than development of new algorithms.
prereq: CSE grad student or instr consent
prereq: CSE grad student or instr consent
Minimum credits
3
Maximum credits
3
Is this course repeatable?
No
Grading basis
OPT - Student Option
Lecture
Requirements
000356
Fulfills the writing intensive requirement?
No
Typically offered term(s)
Fall Even Year