Teaching the next generation of AI experts

All courses and assessments are in English!

Master

• Digital Technologies, Business Analytics and Management (Fachspezifische Grundlagen, FSG)
• AI for Managers (Lecture)

Bachelor

• Introduction to AI (Lecture)
• AI Tools for Management and Social Science (Hauptseminar)

Course Design

In line with our digital focus, we apply a digital and innovative teaching approach and course design. All materials and interactions are coordinated via Moodle. Courses and assessments are in English. We practice an annual rotation of courses, with the exception of the Hauptseminar, which takes place every semester.

Course Sequence

For our Master's courses, we recommend the following sequence (1) AI for Managers [MMT & MBR] or Digital Technologies, Business Analytics and Management [M.Sc. BWL] => (2) Advanced AI in Businesses and Organizations => (3) Research in AI and Management.

We offer inovative and impactful topics (e.g. AI for Good, AI for Medicine, Causal Machine Learning, etc.) that we jointly select to fit the interests and study focus of students. Due to the double-affiliation to the department of Mathematics, Informatics and Statistics, we also welcome students from there to apply for a Bachelor thesis at our chair.

Requirements

• For business students:
  • Basic programming skills
  • Prior participation in our course Introduction to AI (Highly recommended)
• For students in mathematics, informatics and statistics: No requirements

Specifications

• English language
• LaTeX format
• Topics are provided by the chair
• Industry based theses possible, but must be organized by students themselves

Application

• Application via email ai@som.lmu.de
• Provide the following documents and information (1) Curriculum Vitae, (2) transcript, (3) preferred start date

Colloquium

• To support research-oriented focus of the theses, we regularly hold a keynote series around "AI in management". Thesis students are expected to participate.

Tutorial

Download our thesis tutorial (PDF, 179 KB)

For management students (LMU Munich School of Management)

We offer a range of innovative topics around AI tailored for management students. You have the flexibility to choose from different methods—such as literature reviews, meta‐analyses, regression analysis, or machine learning—depending on your interests and research goals. Once you reach out to us, mention explicitly which methods you are interested in. Below are three broad areas you can explore, each with example directions.

1. AI in Business

• Potential Methods: Literature review, meta‐analysis, regression, machine learning
• Example Directions:
  • Investigating how generative AI affects decision‐making in organizations
  • Conducting structured literature reviews on AI use cases

2. AI in the Public Sector

• Potential Methods: Literature review, descriptive analysis, regression
• Example Directions:
  • Examining AI applications to improve public services or policy implementation
  • Mapping innovative AI solutions to address societal challenges (e.g., healthcare, education)

3. Human-AI Collaboration

• Potential Methods: Surveys, field experiments, regression, machine learning
• Example Directions:
  • Surveying user perceptions of generative AI in the workplace
  • Analyzing how AI tools influence teamwork and organizational collaboration

For students at the Faculty of Mathematics, Informatics, and Statistics

Our goal is to foster methodological innovation, encouraging you to explore cutting‐edge techniques—from large language models to advanced causal ML approaches. Below are five focus areas with example directions (which we refine once you have made contact with us):

1. AI in Medicine

Focus: Developing and applying advanced ML methods for improved healthcare outcomes
Potential Methods: Causal ML for treatment effect estimation, diffusion models for patient trajectories, biomarker discovery with deep learning, reliable off‐policy learning

• Example Directions:
  • Causal representation learning to understand how treatments affect patient outcomes
  • Diffusion models for modeling complex or rare disease trajectories

Joint with our collaborators (e.g., LMU Klinikum, Deutsches Herzzentrum München, Cambridge Center for AI in Medicine), we have access to rich and innovative datasets.

2. AI for Good

Focus: Harnessing AI to address global challenges, such as sustainable development and peacebuilding
Potential Methods: Causal ML, advanced ML classification/regression, LLM‐based text analysis, conformal prediction.

• Example Directions:
  • Predicting the impact of development aid on Sustainable Development Goals (e.g., hunger, health, biodiversity) with causal ML
  • Tracking development aid needs with LLMs and real‐time data

3. Innovative ML Methods

Focus: Exploring and advancing core ML techniques
Potential Methods: Diffusion models, continual learning, federated learning, advanced causal ML, novel architecture design

• Example Directions:
  • Designing new diffusion models for complex generative tasks
  • Developing continual learning frameworks to handle data drifts or evolving environments (e.g., joint with Bosch)
  • Federated learning strategies that balance privacy with model performance
  • Developing novel approaches to causal ML for large‐scale or high‐dimensional data

4. Causal ML

Focus: Developing new Causal ML methods for better decision-making
Potential Methods: Causal ML

• Example Directions:
  • Causal representation learning for treatment effects in medicine
  • Causal sensitivity analysis

5. AI for decision-making

Focus: Developing new methods for better decision-making
Potential Methods: Causal ML, offline reinforcement learning, off-policy, optimization

• Example Directions:
  • Reliable off-policy learning for treatment decision-making
  • Off-policy learning for optimizing marketing budgets