SIE575
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SIE575 - Bayesian Machine Learning and Optimal Learning I
Course Description
We consider optimization problems whose objective functions are unknown and hence have to be learned from data. Such problems are pervasive in science and industry, e.g., when
- designing prototypes in engineering,
- automated tuning of machine learning algorithms, e.g., in deep learning,
- optimizing control policies in robotics,
- developing pharmaceutical drugs, and many more.
Bayesian optimization methods are popular in the machine learning community due to their high sample-efficiency and have become a key technique in the area of \"automatic machine learning\". We introduce a general framework in which to understand and formulate such optimal learning problems, and provide a survey of problems, methods, and theoretical results.
- designing prototypes in engineering,
- automated tuning of machine learning algorithms, e.g., in deep learning,
- optimizing control policies in robotics,
- developing pharmaceutical drugs, and many more.
Bayesian optimization methods are popular in the machine learning community due to their high sample-efficiency and have become a key technique in the area of \"automatic machine learning\". We introduce a general framework in which to understand and formulate such optimal learning problems, and provide a survey of problems, methods, and theoretical results.
Min Units
3
Max Units
3
Repeatable for Credit
No
Grading Basis
GRD - Regular Grades A, B, C, D, E
Career
Graduate
Course Attributes
GIDP - STATD (Statistics and Data Science)
Course Requisites
May be convened with
Component
Lecture
Optional Component
No
Typically Offered Main Campus
Fall
Typically Offered Distance Campus
Fall
Typically Offered Online Campus
Fall