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Optimization in Data Science
Provider: Faculty of Science

Activity no.: 5553-19-07-31 
Enrollment deadline: 18/11/2019
PlaceDepartment of Mathematical Sciences
Universitetsparken 5, 2100 København Ø
Date and time18.11.2019, at: 09:00 - 26.01.2020, at: 16:00
Regular seats50
ECTS credits7.50
Contact personNina Weisse    E-mail address: weisse@math.ku.dk
Enrolment Handling/Course OrganiserJonas Martin Peters    E-mail address: jonas.peters@math.ku.dk
Written languageEnglish
Teaching languageEnglish
Semester/BlockBlock 2
Scheme groupB
Exam formOral examination, 25 min
Exam formOral examination
Exam detailsThere will be a 30 min preparation time before the oral exam. Exam registration requirements: The students have to hand-in 5 small group assignments (up to two students), which need to get approved.
Course workload
Course workload categoryHours
Lectures28.00
Theory exercises28.00
Preparation115.00
Exam35.00
Sum206.00
Content
In data science, we can split many problems into two parts. The first part concentrates on finding a class of models that fits well to a data generating process in the real world. In the second part, we then fit the model to the data, which often involves some optimization. The topic of this course is optimization. We derive theory on optimization problems and learn about efficient methodology. We learn how to recognize whether an optimization problem is easy or hard and how to transform problems to have a standard form. Optimization problems arise frequently in many different fields but applications in data science will be our main motivation.

Learning outcome
Knowledge:
- convex sets and functions
- duality
- generalized inequalities
- optimization algorithms
- subgradients

Skills:
- recognizing convex sets and functions
- applying convex relaxations
- solving linear and quadratic programs
- using optimization software

Competences:
- recognizing and transforming optimization problems
- solving different types of optimization problems
- relating optimization to statistics- convex sets and functions
duality
generalized inequalities
optimization algorithms
subgradients

Skills:

recognizing convex sets and functions
applying convex relaxations
solving linear and quadratic programs
using optimization software

Competences:

recognizing and transforming optimization problems
solving different types of optimization problems
relating optimization to statistics

Literature
See Absalon

Teaching and learning methods
4 hours lectures and 4 hours of exercises per week for 7 weeks.

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