Data-driven optimization
Faculteit | Science and Engineering |
Jaar | 2021/22 |
Vakcode | WMME011-05 |
Vaknaam | Data-driven optimization |
Niveau(s) | master |
Voertaal | Engels |
Periode | semester II b |
ECTS | 5 |
Rooster | rooster.rug.nl |
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 |
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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 |
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Uren per week | variabel | ||||||||||||||||
Onderwijsvorm |
Hoorcollege (LC), Werkcollege (T)
(16h LC, 16h T, self study 84h) |
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Toetsvorm |
Practisch werk (PR), Schriftelijk tentamen (WE)
(PR 40% WE 60%) |
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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 |
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