school CITHN2014 - Winter Semester 2025/26

Foundations and Application of Generative AI

Explore modern large language models, multimodal systems, and project-led implementation paths for students who want both technical grounding and responsible deployment thinking.

Official TUM Online arrow_outward Moodle Access login

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Lecturer

Prof. Dr. Chunyang Chen

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Language

English

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Format

Lecture + Exercise

Core Curriculum

Our public syllabus covers the major threads of generative AI, from theoretical models to applied systems.

architecture

Foundations

Language-model basics, transformer architecture, diffusion model, toeknzization, and non-deterministic.

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Prompting

Prompt design, context engineering, evaluation patterns, and quality control.

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Reasoning & Open

Reasoning models, open-source, license, and deployment

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Multimodal

Vision-language systems, and cross-modal workflows.

psychology

AI Agents

Tool use, MCP, orchestration, GUI/coding agents, memory and multi-agent system.

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Security & Privacy

Attack, privacy compliance, mitigation patterns.

Innovation Spotlight

Witness how our students translate course themes into end-to-end prototypes and reflective project delivery.

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Featured Student Project

Career Prep AI

Guest Insights

Deep dives from industry leaders and academic pioneers.

Marcel Gauglitz

Audi

"Transformer architecture."

Junjie Tang

AWS

"Enterprise AI agents."

Student perspective

What Students Say

Selected anonymous EvaSys comments from recent semesters highlight how students experienced the course across projects, tutorials, and invited sessions.

"the concepts that we learnt in class are very interesting, as well as the encouragement of creativity in our project."

WS 2024/25

"I dont have like so much experience in AI and python before, but I think it is so great cause the tutorial is not so difficult for understanding. And this course is related to AI and now I think I have sort of knowledge for this area, so thanks so much!"

WS 2024/25

"There is a big project for the whole course, which is the best way to apply and retain knowledge gained during the lectures and personal research."

SoSe 2025

"The professor starts from the very basics and explained everything at a very appropriate level so that everyone can understand, including those that don't have an IT background."

SoSe 2025

"Invited lecturers to share about related topics, clear and logical teaching as well as patient tutors."

WS 2025/26

"Ich schätze den didaktischen Aufbau und die motivierende Art des Professors sehr. Dadurch fällt das Lernen deutlich leichter."

WS 2025/26

Exponential Growth

The course has seen strong interest across TUM programs, reflecting the demand for technically grounded generative AI education.

2024 Summer 59 students

2024 Winter 80 students

2025 Summer 89 students

2025 Winter 427 students

2026 Summer 210 students (est.)

S24 W24 S25 W25 S26

Course FAQ

Where are slides and weekly files? expand_more

Slides, assignments, and week-to-week operational updates remain in Moodle for enrolled students. This public site does not mirror those files.

How do projects work? expand_more

Students work through a project-centered course format with lectures, exercises, and a final project workflow across the semester.

How is the course assessed? expand_more

Assessment is project-led. The public grading structure centers on the final project, with a smaller assignment or tutorial-track component.

Where should current logistics be checked? expand_more

Lectures are held physically in Heilbronn and live-streamed to Garching, while exercises take place physically in both Heilbronn and Garching. The course is intended for BIE, MIE, and MMDT students in Heilbronn, and for Master of Informatics and Information Systems students in Garching. Current semester logistics and internal teaching operations should still be checked via the official TUM course page and Moodle.