Algorithmic Aspects of Data Analytics and Machine Learning

News

• The Tutorial starts at 12:30 p.m.
• Please contact us if you would like to take an oral exam.

Contents

The lecture addresses algorithmic aspects in the practical realization of mathematical methods in big data analytics and machine learning. The first part will be devoted to the development of recommendation systems, clustering methods and sparse recovery techniques. The architecture and approximation properties as well as the training of neural networks are the subject of the second part. Convergence results for accelerated gradient descent methods for nonsmooth problems will be analyzed in the third part of the course. The lecture is accompanied by weekly tutorials which will involve both, practical and theoretical exercises.

Necessary Prerequisites

Numerik I, II or Basics in Applied Mathematics

Studienleistung/Prüfungsleistung

Studienleistung:

• Obtain at least 50% of possible points in the exercise sheets overall.
• At least one presentation of an exercise on the blackboard during the exercise group. Any request to do so by the tutor must be complied with.

Prüfungsleistung:

• Pass the exam at the end of the semester.

Exercises

Hand in your solutions to the letterbox on the second floor of the Rechenzentrum (mailbox 8). You can submit your solutions in teams of two.

Exercise groups

The tutorial starts in the second week of lectures.

Literature

1. S. Wegner (englisch): Mathematical Introduction to Data Science, Springer, 2024
2. S. Wegner (german): Mathematische Einführung in Data Science, Springer, 2023