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Modulbezeichnung:

Topics in Machine Learning and Data Science

Modulbezeichnung (englisch):

Topics in Machine Learning and Data Science

Modulnummer:

82-105-DS27-H-0924

Niveau:

Bachelor (UNI)

Geberstudiengang:

BA Data Science

Typ:

Modul

Federführende Fakultät/Sprachenzentrum:

Mathematisch-Geographische Fakultät

Modulverantwortliche/r:

Pfander, Götz

Prüfende:

In WS 2025 Götz Pfander

Leistungspunkte (ECTS-Punkte):

Kompetenzen:

Vertiefte Kenntnisse in einem theoretischen oder anwendungsbezogenen Teilgebiet des Maschinellen Lernens oder der Data Science.

Inhalte/Themen:

In the Winter Semester 25, we offer two courses under this umbrella, each can be taken independently and each carries 1 ECTS course credit.

1. Oct/Nov Ilya Krishtal
82-105-DS27-S-VL-0924.20252.002

"Intro to modeling with dynamical systems"

The aim of the course is to enhance the students’ ability to create mathematical models and analyze them. We will focus on models utilizing matrices and linear algebra. We will discuss how basic ideas of spectral theory and sampling play out in this context.

2. Nov/Dec Nils Blümer
82-105-DS27-S-VL-0924.20252.001

“From Language Models to AI Agents”

Since the public release of ChatGPT in November 2022, generative artificial intelligence (AI) approaches based on large language models (LLMs) have gained immense significance. These models power a wide range of applications—from purely text-based systems such as chatbots to multimodal and agentic frameworks involving image recognition, image and video generation, code execution, database queries, and internet search.
This short course introduces students to the foundations and capabilities of transformer-based LLMs, contrasting them with earlier approaches in computational linguistics. We will examine key model features, training paradigms, and inference parameters, aiming to develop a deeper understanding of how LLM-based systems—such as ChatGPT—(often) generate useful and coherent responses.
Building on this foundation, the course will explore advanced topics such as retrieval-augmented generation (RAG), parameter-efficient fine-tuning (PEFT), and the design of multimodal and reasoning-capable models. We will also introduce emerging agentic frameworks, including the Model Context Protocol (MCP), and discuss their potential impact on future AI systems.
Participants are expected to apply course concepts in small experiments or mini-projects, for example using the GWDG LLM platform (https://chat-ai.academiccloud.de/). Results and insights gained from these explorations are to be included in their course summary.

Formale Voraussetzungen für die Teilnahme:

Empfohlene Voraussetzungen:

Lineare Algebra 1, Lineare Algebra 2, Introduction to Programming

Lehr- und Lernformen/Lehrveranstaltungstypen:

Vorlesung (VL) (8h)
selbstgeleitetes Lernen (SGL)

Voraussetzungen für die Vergabe von ECTS-Punkten:

2-5 Seiten Zusammenfassung des Kurses

Zeitaufwand/Verteilung der ECTS-Punkte innerhalb des Moduls:

Vorlesung: 0,5 ECTS Punkte entspricht 8-10 Stunden
Vor- und Nachbereitung: 0,25 ECTS-Punkte entspricht 5 Stunden
Leistungsnachweis Zusammenfassung des Kurses: 0,25 ECTS-Punkt(e), entspricht 5 Stunden

Modulnote:

Leistungsnachweis: Bestanden / nicht bestanden

Lehr- und Lernmethode:

Polyvalenz mit anderen Studiengängen/Hinweise zur Zugänglichkeit:

B.Sc. Digital and Data Driven Business
B.Sc. Mathematik

Turnus des Angebots:

WS , SS

Beteiligte Fachgebiete:

Data Science und Mathematik

Bemerkung: