Machine Learning for Decision Makers
Certificate Course - Financial Engineering - FE.3.1.I25
| Date | Jun 10-11, 2026 |
| Duration | 2 days |
| Location | On campus - Karlsruhe |
| Language | English |
| ECTS | Upon request |
| Cost | 1,550 € |
Fundamentals
Understand core machine learning concepts and how algorithms improve predictive accuracy across different business applications.
Technology
Learn key algorithm families including information, similarity, probability, and error-based methods for training predictive models.
Applications
Apply machine learning techniques to real business problems, selecting appropriate models to uncover robust predictive relationships.
What you´ll explore
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Explore the fundamentals of inductive machine learning and its role in predictive analytics.
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Understand how different algorithm families approach learning and pattern recognition.
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Learn when and how to apply information-based, similarity-based, probability-based, and error-based methods.
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Analyze how machine learning models are trained to improve forecasting performance.
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Discover applications of machine learning across various business disciplines.
Your key takeaways
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Understanding of key machine learning concepts and predictive modelling
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Ability to distinguish between major learning algorithm families
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Skills to select appropriate models for different business problems
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Knowledge of how algorithms improve forecasting accuracy
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Practical insight into training models to detect robust patterns
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Foundation for applying machine learning in business and analytics
Taught by recognized experts in Machine Learning for Decision Makers
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 course is particularly beneficial for professionals in the following fields:
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Machine learning engineers and AI researchers
Professionals developing ML models, optimizing algorithms, or advancing pattern recognition techniques in tech, research, or industry-specific applications. -
Data scientists and analytics experts
Analysts leveraging ML for predictive modeling, data-driven decision-making, or pattern extraction in sectors like finance, healthcare, or automation. -
Computer vision engineers and specialists
Professionals focused on image, video, or sensor data analysis, object detection, and pattern recognition in autonomous systems or AI-driven applications. -
Software developers integrating AI
Engineers embedding ML and pattern recognition into software, platforms, or embedded systems for enhanced functionality. -
Researchers and academics in AI and CS
Researchers, PhD candidates, or academics exploring advancements in ML, deep learning, reinforcement learning, or pattern recognition. -
Industry specialists in automation and robotics
Professionals in robotics, smart manufacturing, or IoT who apply ML to improve system performance, adaptability, or autonomy. -
Consultants in AI, data strategy, or digital transformation
Advisors guiding organizations in adopting ML, optimizing AI workflows, or implementing pattern recognition solutions.
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.