Course syllabus for Continuous optimization in data science

Course syllabus adopted 2023-02-02 by Head of Programme (or corresponding).

Overview

  • Swedish nameKontinuerlig optimering inom datascience
  • CodeDAT570
  • Credits7.5 Credits
  • OwnerMPDSC
  • Education cycleSecond-cycle
  • Main field of studyComputer Science and Engineering, Software Engineering
  • DepartmentCOMPUTER SCIENCE AND ENGINEERING
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 87125
  • Block schedule
  • Open for exchange studentsYes

Credit distribution

0123 Written and oral assignments 7.5 c
Grading: TH		0 c7.5 c0 c0 c0 c0 c

In programmes

Examiner

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Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

To be eligable for the course the students shall have 7.5 credits from courses in programming in a general-purpose programming language or
equivalent (preferably, but not limited to Python), and 7.5 credits mathematics or statistics.

Aim

The purpose of this course is to provide knowledge about the continuous optimization toolset for solving data science problems. After completing this course, the students are capable of formulating various data science problems as optimization problems and design a proper an approriate algorithm to solve them, considering computational complexity and statistical performance. 

Learning outcomes (after completion of the course the student should be able to)

Knowledge and understanding:
  • describe different types of optimization problems, such as continuous/discrete/mixed, convex/nonconvex
  • explain what types of data science problems can be addressed by optimization problems
  • explain the main principles of different optimization algorithms and their global/local convergence
  • account for the computational complexity of optimization algorithms in data science, as well as their performance
Skills and abilities:
  • implement various optimization algorithms as computer programs,
  • apply and adapt optimization algorithms to data science problems, such as machine learning and rule-based approaches
  • find approximate solutions to computationally hard problems
  • formulate various problems in data science as mathematical optimization problems
Judgement ability and approach:
  • reason about what type of information or features of the data could be useful in selecting optimization algorithms
  • select the appropriate evaluation methodology including performance and convergence analysis

Content

The course discusses the main aspects of optimization problems in data science, namely the concept of convergence and its relation to the statistical learning theory. The course presents different classifications of optimization problems such as convex/noncovex. The course also includes various challenges in data science by presenting real-world examples, and discusses main algorithmic ideas to address them.

Organisation

Lectures, exercise sessions, computer lab sessions

Literature

Course literature to be announced the latest 8 weeks prior to the start of the course.

Examination including compulsory elements

The course is assessed through compulsory assignments.

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.