Continuous Optimization in Data Science

Education facts

Code: DIT762

<p>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.</p> <p><em>Sub-courses</em><br /> <strong>1. Written hall examination </strong><em>(Skriftlig salstentamen)</em>, 4 credits<br /> Grading scale: Pass with distinction (5), Pass with credit (4), Pass (3) and Fail (U)<br /> <br /> <strong>2. Assignments</strong> <em>(Inlämningsuppgifter)</em>, 3.5 credits<br /> Grading scale: Pass with distinction (5), Pass with credit (4), Pass (3) and Fail (U)</p>

Entry requirements

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. Applicants must prove knowledge of English: English 6/English B or the equivalent level of an internationally recognized test, for example TOEFL, IELTS.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Continuous Optimization in Data Science

  University of Gothenburg

  Start date: 4 November 2024

  End date: 19 January 2025

  Pace of study: 50 %