Winterthur_ZHAW

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Zürich

max. 10'000 CHF

Certificate of Advanced Studies (CAS)

Artificial intelligence and machine learning are reshaping finance from portfolio construction to risk and algorithmic trading. Yet, experts who bridge theory and real-world use are few. Our CAS in Machine Learning for Portfolio and Risk Management closes that gap with practical, industry-focused training for finance professionals.

1 - 6 Monate

After completing the programme, participants will be able to:<ul><li>Develop, test, and validate machine learning models for investment strategies and risk management.</li><li>Apply data-driven approaches to portfolio optimisation.</li><li>Navigate regulatory, ethical, and technical challenges in ML projects.</li><li>Collaborate effectively across disciplines with data scientists, portfolio managers, and compliance professionals.</li></ul>With its strong ties to the finance industry and close collaboration with leading experts, this CAS offers immediate value to practitioners looking to integrate machine learning into their workflows in a meaningful way. It stands out in the Swiss continuing education landscape and helps drive innovation in the financial sector.

Objectives

This programme is designed for:<ul><li>Portfolio and risk managers aiming to develop or enhance data-driven models for portfolio construction and risk management.</li><li>Investment analysts and finance professionals looking to integrate machine learning into forecasting, risk assessment, and alpha generation.</li><li>Quants and data scientists with technical skills who want to deepen their understanding of ML applications in finance.</li><li>IT and tech specialists in financial institutions working on the implementation and optimisation of ML-driven systems in trading, investment, and risk.</li><li>Experienced business and strategy consultants seeking to better understand the potential of ML and AI in enabling data-informed decision-making within financial organisations.</li><li>Project leads managing innovative ML-based financial solutions who want to navigate the specific challenges of ML projects in finance and integrate them successfully into existing processes.</li><li>Finance and business professionals with a strong entrepreneurial mindset, looking to implement and scale AI/ML-driven strategies in their own organisations.</li></ul>

Target audience

Module: Foundations of Machine Learning in FinanceUpon completing the programme, participants will:<ul><li>Gain a solid foundation in Python as a tool for data analysis and financial application.</li><li>Understand the core principles of supervised and unsupervised learning, as well as feature engineering tailored to financial datasets.</li><li>Become familiar with key ML models for portfolio construction and risk management, including Markowitz optimisation and factor modelling.</li><li>Be aware of the importance of data quality, security, and regulatory compliance in financial applications.</li><li>Be equipped to design data-driven portfolio strategies and critically assess machine learning-based decision models.</li></ul>Module: Advanced Machine Learning ApplicationsUpon completing the programme, participants will:<ul><li>Be familiar with advanced ML techniques such as Lasso, SVMs, decision trees, and neural networks for financial applications.</li><li>Gain deep insights into ML-based risk models, including Value at Risk, stress testing, and anomaly detection.</li><li>Be able to design, backtest, and evaluate algorithmic trading strategies using relevant performance metrics.</li><li>Understand model validation techniques and how to stress-test and enhance the robustness of ML models.</li><li>Explore cutting-edge research topics, particularly Explainable AI (XAI), to improve model transparency and interpretability.</li><li>Benefit from an expanded professional network across finance, tech, and machine learning communities.</li></ul>

Content

<ul><li>Expanded leadership opportunities in the financial sector.</li><li>Specialization in managing AI projects.</li><li>Enhanced career prospects as a Data Scientist or Risk Manager.</li><li>Potential to drive innovation within corporate environments. </li></ul>

Career Perspective

In-person teaching with a variety of methods such as discussions, presentations, exercises, and case studies.Use of exchange and networking with leaders from practice.

Teaching Model

Englisch

auszeichnung

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Transparente Schule

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Zürcher Hochschule für Angewandte Wissenschaften

Fachhochschulen (FH) wie z.B. die FHNW in Olten und Windisch, oder die Zürcher Hochschule für angewandte Wissenschaften ZHAW bieten tendenziell praxisorientierte Fächer bzw. Studiengänge und Weiterbildungen an. Diese Hochschulform steht daher in engem Austausch mit der Arbeitswelt und fördert die angewandte Forschung, Berufspraktika und Praxis-Projekte. Eine FH ist meist anwendungsorientierter als eine Uni. Zudem sind Studierende einer FH in den meisten Fällen in einem persönlicheren Kontakt mit den Dozierende, als Studierende einer Uni.

CAS Machine Learning for Advanced Portfolio and Risk Management

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