Data-driven optimization

Uitgebreide vaknaam	Data-driven optimization
Leerdoelen	At the end of the course, the student is able to: 1) identify various data-driven optimization models and their relevant applications 2) explain and apply basics on probability and optimization 3) explain and implement various data-driven optimization methods 4) explore advanced data-driven optimization methods
Omschrijving	The objective of the course is to understand the fundamentals and relevant applications of data-driven optimization. Particular focus is on machine learning and control theory related fields. The tentative list of topics is: - Introduction to data-driven optimization models - Linear and logistic regression - Support vector machines - Gradient and stochastic gradient descent - Online optimization - Time permitting, Gaussian processes, Neural networks, and Kernel methods
Uren per week	variabel
Onderwijsvorm	Hoorcollege (LC), Werkcollege (T) (16h LC, 16h T, self study 84h)
Toetsvorm	Practisch werk (PR), Schriftelijk tentamen (WE) (PR 40% WE 60%)
Vaksoort	master
Coördinator	Dr. A.K. Cherukuri
Docent(en)	Dr. A.K. Cherukuri
Entreevoorwaarden	The course assumes familiarity with linear algebra, calculus, and preliminary notions on optimization and probability.
Opmerkingen	This course was previously registered with course code WMME19011
Opgenomen in	Opleiding Jaar Periode Type MSc Industrial Engineering and Management: Production Technology and Logistics  (Optional technical PTL modules) - semester II b Elective PLE/SSCA MSc Mechanical Engineering: Advanced Instrumentation  (Electives ) 1 semester II b keuzegroep MSc Mechanical Engineering: Smart Factories  (Electives ) 1 semester II b keuzegroep