AI and Machine Learning
AI and Machine Learning
Modulleitung
Studiengänge
Masterstudiengang Innovative Finance (MSc IF 24)
(01.09.2024)
The course "AI and Machine Learning in Finance" introduces students to the exciting world of artificial intelligence and machine learning, specifically focusing on their application in investment strategies. Building on basic concepts learned in previous courses, this course explores essential methods of supervised and unsupervised learning, teaching students to analyse large datasets for innovative financial insights. The course includes practical R -codes to help students set up their own ML-driven investment strategies.
Key topics covered in this course include:
Key topics covered in this course include:
- Introduction to linear model selection and regularization
- Resampling methods and model assessment
- Basics of tree-based methods
- Fundamentals of neural networks and deep learning
- Essentials of unsupervised learning
- Applying machine learning techniques to factor investing
Lehrmethode
- Interactive lectures
- Hands-on coding sessions and workshops
- Real-world data applications and projects using Colab and Kaggle
- Group discussions and presentations
- Case studies of ML-driven investment strategies.
Lernergebnisse
After successful completion of the course, students will:
Professional competence
Methodological competence
Social competence
Personal competence
Technological competence
Professional competence
- understand the basics of supervised and unsupervised learning in finance.
- recognize how machine learning techniques can be applied to investment strategies.
- gain practical experience in using ML-driven approaches for analysing financial data.
Methodological competence
- apply fundamental machine learning techniques for financial data analysis.
- use R and provided R-codes to create and test ML-driven investment strategies.
- assess the performance of machine learning models in financial applications.
Social competence
- work effectively in teams on projects related to machine learning in finance.
- communicate basic machine learning concepts and their financial applications.
- participate in discussions on the ethical use of AI in finance.
Personal competence
- engage in self-directed learning and research in machine learning and finance.
- develop problem-solving skills for financial data challenges.
- reflect on the role of AI in modern finance.
Technological competence
- gain confidence in using R for implementing machine learning models.
- apply technology to enhance financial analysis and decision-making.
Literatur
- Coqueret, G. &Guida, T. (2020). Machine Learning for Factor Investing. Chapman and Hall. Available online at www.mlfactor.com
Prüfungsmodalitäten
Short paper presentation (10%), Homework presentation (40%), Empirical project (50%); Attendance is mandatory (80%)