Kernel and Bayesian Methods in Machine Learning
Certificate Course - Financial Engineering - FE.3.3.I25
| Date | Jun 18-19, 2026 |
| Duration | 2 days |
| Location | On campus - Karlsruhe |
| Language | English |
| ECTS | Upon request |
| Cost | 1,550 € |
Fundamentals
Understand kernel methods and Bayesian learning for forecasting, parameter estimation, and probabilistic reasoning in machine learning.
Technology
Apply Python-based kernel methods and Bayesian models to estimate parameters and perform semi-parametric forecasting tasks.
Applications
Use kernel and Bayesian learning techniques for data-driven forecasting, state estimation, and solving real-world machine learning problems.
What you´ll explore
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Explore kernel methods for describing data and generating semi-parametric forecasts.
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Understand Bayesian learning as an alternative approach to parameter estimation and inference.
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Learn how kernel methods are applied in predictive modeling tasks.
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Analyze how Bayesian frameworks are used to learn parameters and filter state variables.
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Discover how probabilistic reasoning improves machine learning performance.
Your key takeaways
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Understanding of kernel methods and Bayesian learning foundations
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Ability to apply kernel-based models for forecasting tasks
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Knowledge of Bayesian approaches to parameter and state estimation
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Skills to implement kernel and Bayesian methods in Python
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Competence in probabilistic reasoning for machine learning problems
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Practical experience applying semi-parametric forecasting techniques
Taught by Recognized Experts in Kernel and Bayesian Methods in 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.
About HECTOR School
HECTOR School, the Technology Business School of the Karlsruhe Institute of Technology (KIT), is a leading provider of executive education in technology-driven fields.
For this course, participants who successfully complete the examination can earn a KIT certificate with ECTS credits, which may be credited toward our Executive Master of Science or Advanced Studies Programs, subject to content alignment.