Die nächste Generation von KI Experten

Alle Kurse und Prüfungen sind in englischer Sprache!

Master

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

Bachelor

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

Kurs Design

Im Einklang mit unserem digitalen Fokus wenden wir einen digitalen Lehransatz und innovatives Kursdesign an. Alle Materialien und Interaktionen werden über Moodle koordiniert. Die Kurse und Prüfungen sind in englischer Sprache. Die Lehrveranstaltungen werden im jährlichen Wechsel angeboten, mit Ausnahme des Hauptseminars, das jedes Semester stattfindet.

Kurs Reihenfolge

Für unsere Master Kurse empfehlen wir die folgende Reihenfolge: (1) AI for Managers [MMT & MBR], Digital Technologies, Business Analytics and Management [M.Sc. BWL], (2) Advanced AI in Businesses and Organizations, (3) Research in AI and Management.

Wir bieten innovative und interessante Themen (z.B. AI for Good, AI for Medicine, Causal Machine Learning, etc.), die wir gemeinsam entsprechend den Interessen und Schwerpunkten der Studierenden auswählen. Aufgrund der doppelten Zugehörigkeit zur Fakultät für Mathematik, Informatik und Statistik sind auch Studierende dieser Bereiche willkommen, sich für eine Bachelorarbeit an unserem Lehrstuhl zu bewerben.

Voraussetzungen

• Für BWL Studierende:
  • Grundlagen Programmierkenntnisse
  • Vorherige Teilnahme am Kurs Introduction to AI (Empfohlen)
• Für Studierende der Mathematik, Informatik oder Statistik: Keine Voraussetzung

Spezifikationen

• Englische Sprache
• LaTeX Format
• Themen werden vom Lehrstuhl vorgegeben
• Abschlussarbeiten in Kooperation mit Unternehmen sind möglich, müssen aber von den Studierenden selbst organisiert werden

Bewerbung

• Bewerbung per E-Mail ai@som.lmu.de
• Folgende Unterlagen und Informationen sind einzureichen (1) Curriculum Vitae, (2) Transcript, (3) Bevorzugtes Start Datum

Kolloquium

• Um den forschungsnahen Charakter der Abschluss zu gewähren, wird erwartet, dass Studierende auch an unseren regelmäßigen Keynotes zu "AI in Management" teilnehmen

Tutorium

Download unseres Thesis Tutoriums (PDF, 179 KB) (Englisch)

BWL Studierende

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

Mathematik, Informatik und Statistik Studierende

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:

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