This programme is designed for:

• Portfolio and risk managers aiming to develop or enhance data-driven models for portfolio construction and risk management.
• Investment analysts and finance professionals looking to integrate machine learning into forecasting, risk assessment, and alpha generation.
• Quants and data scientists with technical skills who want to deepen their understanding of ML applications in finance.
• IT and tech specialists in financial institutions working on the implementation and optimisation of ML-driven systems in trading, investment, and risk.
• Experienced business and strategy consultants seeking to better understand the potential of ML and AI in enabling data-informed decision-making within financial organisations.
• 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.
• Finance and business professionals with a strong entrepreneurial mindset, looking to implement and scale AI/ML-driven strategies in their own organisations.

After completing the programme, participants will be able to:

• Develop, test, and validate machine learning models for investment strategies and risk management.
• Apply data-driven approaches to portfolio optimisation.
• Navigate regulatory, ethical, and technical challenges in ML projects.
• Collaborate effectively across disciplines with data scientists, portfolio managers, and compliance professionals.

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.

Upon completing the programme, participants will:

• Gain a solid foundation in Python as a tool for data analysis and financial application.
• Understand the core principles of supervised and unsupervised learning, as well as feature engineering tailored to financial datasets.
• Become familiar with key ML models for portfolio construction and risk management, including Markowitz optimisation and factor modelling.
• Be aware of the importance of data quality, security, and regulatory compliance in financial applications.
• Be equipped to design data-driven portfolio strategies and critically assess machine learning-based decision models.

Upon completing the programme, participants will:

• Be familiar with advanced ML techniques such as Lasso, SVMs, decision trees, and neural networks for financial applications.
• Gain deep insights into ML-based risk models, including Value at Risk, stress testing, and anomaly detection.
• Be able to design, backtest, and evaluate algorithmic trading strategies using relevant performance metrics.
• Understand model validation techniques and how to stress-test and enhance the robustness of ML models.
• Explore cutting-edge research topics, particularly Explainable AI (XAI), to improve model transparency and interpretability.
• Benefit from an expanded professional network across finance, tech, and machine learning communities.

Springsemester 2026.

The programme is worth 12 ECTS credits and is designed to be taken alongside a job. It runs for approximately 5 months.

The maximum duration of study is 2 years. Extensions may be granted by the programme director in justified cases.
 

Admission Requirements

For applicants with a university degree

To be admitted to the certificate programme, applicants must meet the following criteria:

• A degree (diploma, licentiate, bachelor's or master's) from a state-accredited university or a recognised predecessor institution.
• At least 3 years of relevant professional experience at the time of application.
• Alternatively, a comparable qualification and at least 5 years of professional experience

The programme director reserves the right to invite applicants for an interview and to request references.
 

For applicants without a university degree

Applicants without a university degree may be admitted if they fulfil the following:

• A recognised tertiary-level B qualification (higher vocational education), such as a Federal Diploma of Higher Education (eidg. Fachausweis), Advanced Federal Diploma of Higher Education (eidg. Diplom), or a diploma from a college of higher education (HF).
• At least 5 years of relevant professional experience following initial vocational training.
• In exceptional cases, individuals with alternative qualifications may be admitted if their ability to participate can be demonstrated through other means
• For applicants without a university degree, a formal admission interview is a mandatory part of the selection process.
   

Admissions are decided on by the Head of Program.
 

Registrations receive consideration in the order they were received.