Fundamentals of Financial Machine Learning
Certificate Course - Financial Engineering - FE.3.2.I26
| Date | May 31- Jun 02 and Jun 07-09, 2027 |
| Duration | 6 days |
| Location | On campus - Karlsruhe |
| Language | English |
| ECTS | Upon request |
| Cost | 3,150 € |
Fundamentals
Understand financial machine learning, time series and cross-sectional models, factor models, and volatility prediction methods.
Technology
Apply Python and algorithms such as ARMA, GARCH, Kalman Filter, and State Space Models to model returns and volatility.
Applications
Develop models for risk premia, asset pricing, and volatility forecasting, using backtesting and machine learning in finance.
What you´ll explore
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Explore algorithmic and Python-based financial machine learning techniques.
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Understand time-series and cross-sectional modeling of returns and volatility.
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Learn ARMA, GARCH, and stochastic volatility models for forecasting financial data.
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Analyze factor models and their strengths and limitations in asset pricing.
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Study State Space Models and the Kalman Filter for estimating hidden variables.
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Discover how machine learning is used to identify risk premia and market patterns.
Your key takeaways
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Understanding of financial machine learning methods and model selection
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Ability to apply time-series and cross-sectional approaches to financial data
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Knowledge of ARMA, GARCH, and stochastic volatility models
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Skills to use Python for model calibration and financial analysis
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Competence in applying factor models and evaluating their performance
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Experience with Kalman Filter and State Space Models
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Ability to perform cross-validation, backtesting, and hyperparameter tuning
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Practical skills in risk and asset management using machine learning
Taught by Recognized Experts in Fundamentals of Financial Machine Learning
Benefit from the knowledge of leading specialists with extensive experience in research and industry. Their deep expertise guarantees a course of outstanding academic and practical quality.
Prof. Dr. Maxim Ulrich
Prof. Dr. Maxim Ulrich is Professor of Risk Management and Financial Economics at KIT and has built an international academic career, including serving as Tenure-Track Assistant Professor at Columbia Business School from 2008. His work focuses on quantitative finance, asset pricing, and financial machine learning, making him a recognized expert in the field.
He brings extensive experience from academia and practice, including fintech ventures and collaborations with institutions such as the ECB and Eurex.
Who Should Attend
This module is designed for professionals seeking a structured introduction to machine learning and its applications in decision-making, finance, and advanced analytical modeling.
- Decision-makers and managers interested in understanding machine learning concepts and methods
- Finance professionals seeking data-driven approaches to market analysis and forecasting
- Analysts and researchers looking to strengthen their knowledge of applied and advanced machine learning techniques
- Professionals who want a coherent learning path from foundational to specialized topics
Advance your career with KIT-level expertise
Benefit from the reputation of the Karlsruhe Institute of Technology (KIT) while gaining practical skills, flexible learning opportunities, and a recognized certificate to support your long-term professional growth.
Flexibility
Gain focused expertise in a specific field without committing to a full degree program, allowing you to build relevant knowledge efficiently and integrate learning seamlessly into your professional routine.
Relevance
Benefit from high-quality academic content combined with practical insights, delivered by experienced experts, supporting continuous, lifelong learning while ensuring direct applicability in real-world scenarios.
Advancement
Enhance your professional profile with a recognized certificate, demonstrating your commitment to ongoing development and supporting your career with tangible, verifiable credentials.