Course Schedule
Veranstaltungen von Akademischer Rat Debayan Banerjee
Lehrveranstaltungen
KI-Projekt (Seminar)
Dozent/in: Debayan Banerjee, Kai Moltzen, Ricardo Usbeck
Termin:
wöchentlich | Donnerstag | 14:00 - 16:30 | 13.10.2025 - 30.01.2026 | C 5.109 Seminarraum
Einzeltermin | Do, 30.10.2025, 14:00 - Do, 30.10.2025, 16:30 | C 12.112 Seminarraum
Inhalt: Wir werden in dem Projekt u.a. Methoden aus der Vorlesung ‚Einführung in die Künstliche Intelligenz‘ anwenden, um exemplarische Problemstellungen zu lösen. Hierbei kommen auch Methoden aus dem Bereich NLP, insbesondere Large Language Models (!) zum Einsatz. Dabei bearbeiten die Studierenden ein selbstgewähltes Projekt in Gruppen (von bis zu 3 Personen) und erarbeiten sich von den Daten über die Demo bis zum Business Plan mit Hilfe von Mentoring alles selbst.
Advanced Machine Learning (Vorlesung)
Dozent/in: Debayan Banerjee, Kai Moltzen, Ricardo Usbeck
Termin:
wöchentlich | Donnerstag | 10:15 - 11:45 | 13.10.2025 - 30.01.2026 | C 6.026 Seminarraum
Einzeltermin | Do, 05.03.2026, 10:15 - Do, 05.03.2026, 11:45 | C 6.026 Seminarraum | exam date: written test
Inhalt: TBA! Old: The course introduces Natural Language Processing (NLP), classic models such as RNNs, and proceeds to modern models such as Transformers. Pre-trained and large language models (LLMs) are introduced and methods to use them using prompt engineering will be taught. In later half of the course, Knowledge Graphs (KGs) will be introduced, and how Retrieval Augmented Generation (RAG) techniques can be used to perform Question Answering (QA) over Knowledge Graphs using LLMs.
- Masterprogramm Management: Management & Sustainable Accounting and Finance - Alternative Wahlmodule - Special Topics in Data Science
- Masterprogramm Management: Management & Entrepreneurship - Alternative Wahlmodule - Special Topics in Data Science
- Masterprogramm Management: Management & Engineering - Alternative Wahlmodule - Special Topics in Data Science
- Masterprogramm Management: Management & Data Science - Facheigene Wahlmodule - Special Topics in Data Science
DATAx: Data analysis with Python (6) (Übung)
Dozent/in: Debayan Banerjee
Termin:
wöchentlich | Montag | 15:00 - 15:55 | 13.10.2025 - 30.01.2026 | C 16.129 Seminarraum
Inhalt: THE COURSE WILL BE HELD ON ZOOM. Meeting-ID: 98951686809 Password: dataxdb This exercise introduces programming and data analysis using the Python programming language. It is specifically designed for students with no prior programming knowledge or experience. During the course, students will learn: - Basic programming concepts, such as variables, conditions, loops and functions. - Effective use of large language models (LLMs) in chat AI. - The essential steps for performing data analysis with Python. With the help of ready-made Jupyter notebooks, instructors from different disciplines supervise students' first practical experience of using Python in Jupyter notebooks, including sessions on data analysis and machine learning. Regular assignments encourage students to gain hands-on experience in programming and data analysis, and to apply their newfound knowledge to their field of study. By the end of the semester, students will work in a study group on a data-driven project, taking on different roles and learning to extract and present insights from real data together. Throughout the exercise, experienced and dedicated tutors (Teaching Assistants) are available to support students on campus. The languages of instruction in the tutorials are German and English.
DATAx: Data analysis with Python (5) (Übung)
Dozent/in: Debayan Banerjee
Termin:
wöchentlich | Montag | 14:00 - 14:55 | 13.10.2025 - 30.01.2026 | C 16.129 Seminarraum
Inhalt: THE COURSE WILL BE HELD ON ZOOM. Meeting-ID: 94254311581 Password: dataxdb This exercise introduces programming and data analysis using the Python programming language. It is specifically designed for students with no prior programming knowledge or experience. During the course, students will learn: - Basic programming concepts, such as variables, conditions, loops and functions. - Effective use of large language models (LLMs) in chat AI. - The essential steps for performing data analysis with Python. With the help of ready-made Jupyter notebooks, instructors from different disciplines supervise students' first practical experience of using Python in Jupyter notebooks, including sessions on data analysis and machine learning. Regular assignments encourage students to gain hands-on experience in programming and data analysis, and to apply their newfound knowledge to their field of study. By the end of the semester, students will work in a study group on a data-driven project, taking on different roles and learning to extract and present insights from real data together. Throughout the exercise, experienced and dedicated tutors (Teaching Assistants) are available to support students on campus. The languages of instruction in the tutorials are German and English.