Continuous Optimization in Data Science

Utbildningsfakta

Kod: 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>

Behörighet

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.

Utbildningstillfällen

Varje tillfälle har egna datum och villkor. Avslutade tillfällen behålls som historik och innebär inte att en ny ansökan är öppen.

• Continuous Optimization in Data Science

  University of Gothenburg

  Startdatum: 4 november 2024

  Slutdatum: 19 januari 2025

  Studietakt: 50 %