|
|
Vorlesungsverzeichnis >> Fakultät Wirtschaftsinformatik und Angewandte Informatik >> Bachelor-/Masterstudiengänge Angewandte Informatik, Computing in the Humanities, International Information Systems Management, Software Systems Science, International Software Systems Science, Wirtschaftsinformatik, Wirtschaftspädagogik mit Schwerpunkt Wirtschaftsinformatik >>
|
xAI-MML-M Mathematics for Machine Learning- Dozent/in
- Prof. Dr. Christian Ledig
- Angaben
- Vorlesung
Rein Präsenz
2,00 SWS, Unterrichtssprache Englisch
Zeit und Ort: Di 12:00 - 14:00, WE5/00.019
- Voraussetzungen / Organisatorisches
- Degree Programs: MSc AI/CitH/WI/ISSS
No specific prior knowledge is required, but the following will be helpful.
- Working knowledge of programming (e.g., in Python).
- Completion of mathematical courses addressing concepts of linear algebra (e.g., KTR-MfI-2), calculus (e.g., WiMa-B-002), or statistics (e.g., Stat-B).
- Inhalt
- Content
The course aims to establish a common mathematical foundation for the further study of advanced machine learning techniques. The content is selected specifically to be most relevant for students interested in machine learning problems and covers a broad range of concepts from, e.g., linear algebra, vector calculus, probability theory, statistics, and optimization.
Goals
In this course students will learn fundamental mathematical concepts that are important prerequisites for the deeper understanding of the field of machine learning. The overarching goal of this course is to build a mathematical foundation by selectively covering the most essential mathematical concepts form a broad range of mathematical disciplines. Dependent on previous background, students will get the chance to learn critical ML-relevant mathematics for the first time or consolidate concepts that have been partially covered in their previous curriculum.
The lecture is accompanied by exercises and assignments that will help participants develop both theoretical and practical experience. In those exercises students will get the opportunity to learn how to apply and prove theoretical concepts as well as implement some concrete algorithms in Python and its respective commonly used libraries.
- Empfohlene Literatur
- Marc. Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong: Mathematics for Machine Learning, Cambridge University Press, 2020
Further literature will be announced at the beginning of the course.
- Englischsprachige Informationen:
- Title:
- xAI-MML-M Mathematics for Machine Learning
- Credits: 6
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 50
- Institution: Lehrstuhl für Erklärbares Maschinelles Lernen
Hinweis für Web-Redakteure:
Wenn Sie auf Ihren Webseiten einen Link zu dieser Lehrveranstaltung setzen möchten, verwenden Sie bitte einen der folgenden Links:Link zur eigenständigen Verwendung Link zur Verwendung in Typo3
|
 |
|
|
UnivIS ist ein Produkt der Config eG, Buckenhof |
|
|