Andrea Ferrario: Catalogue data in Spring Semester 2025 |
| Name | Dr. Andrea Ferrario |
| Address | RiskLab ETH Zürich, HG F 42.1 Rämistrasse 101 8092 Zürich SWITZERLAND |
| aferrario@ethz.ch | |
| Department | Management, Technology, and Economics |
| Relationship | Lecturer |
| Number | Title | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||
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| 363-1098-00L | Business Analytics Students from the MAS MTEC are not applicable for this course and are kindly asked to enroll in the course "AI for Executives (365-1120-00L)" instead. | 3 credits | 2G | A. Ferrario | |||||||||||||||||||||||||||||
| Abstract | In this course, students learn to plan, implement and evaluate analytics in applied settings to generate value from data for society, corporations and individuals. This serves the pressing need of firms to improve their efficiency – such as customer satisfaction, competitive advantage –by leveraging the growing amounts of structured and unstructured data and methods, such as machine learning. | ||||||||||||||||||||||||||||||||
| Learning objective | Overall learning goal By the end of the course, students will be able to plan, implement and evaluate analytics in applied settings in order to generate value from data for society, corporations and individuals. This serves the pressing need of firms to improve their efficiency – such as customer satisfaction, competitive advantage –by leveraging the growing amounts of structured and unstructured data and methods, such as machine learning. Detailed breakdown by objective To achieve this overall goal, students should after participation being able to: Objective 1 (Managerial aspects): Understand the processes and challenges of analytics-related projects • Identify applications for analytics in corporations and organizations that create value • List implications for management when undertaking a project involving business analytics • Apply the data mining process CRISP-DM to their actual setting Objective 2 (Methodological challenges): Understand common methods for performing business analytics • Translate use cases of business analytics into a mathematical model formulation • Name common methods for business analytics, as well as their underlying concepts • Compare the properties of these models and perform performance assessment Objective 3 (Practical implementation): Performing actual evaluations of business analytics based on real-word datasets • Preprocess data in order to transform it into relational structures • Apply statistical software (e.g. “R” or Python) to perform business analytics in practice • Evaluate the results in order to choose the best-performing method | ||||||||||||||||||||||||||||||||
| Content | With the emergence of ubiquitous computing technology and machine learning methods in industrial applications, company decisions nowadays rely strongly on computer-aided “Business Analytics”. Business analytics refers to technologies that target how business information (or sometimes information in general) is collected, analyzed and presented. Combining these features results in software serving the purpose of providing better decision support for individuals, businesses and organizations. This course will teach what distinguishes the varying capabilities across business analytics – namely the underlying methods (e.g., machine learning). Participants will learn different strategies for data collection, data analysis, and data visualization. Sample approaches focus on machine learning modeling and machine learning pipelines to support business analyrics projects. In particular, the course will teach the following themes: • Forecasting/Predicting: How can historical values be used to make predictions of future developments ahead of time? How can firms utilize unstructured and structured data to support the predictive performance? What are metrics to evaluate the performance of predictions? How to embed machine learning model predictions in business projects? • Data analysis: How can one derive explanatory power in order to study the response to an input? Note: the course provides the theoretical elements of business analytics projects. This provides then the basis for a project work where groups of students propose and implement analytics to business-relevant datasets. This project underlies eventually the grading. | ||||||||||||||||||||||||||||||||
| Lecture notes | Content: 1. Motivation and terminology: fundamentals of Business Analytics 2. Examples of Business Analytics projects 3. Key elements of Business Analytics projects 4. Methods of Business Analytics (CRISP-DM) (e.g., data collection, data processing, machine learning modeling, model evaluation, managerial implications) 5. Collaborating in Business Analytics projects | ||||||||||||||||||||||||||||||||
| Literature | James, Witten, Hastie & Tibshirani (2013): An Introduction to Statistical Learning: With Applications in R. Springer. Sharda, Delen & Turban (2014): Business Intelligence: A Managerial Perspective on Analytics. Pearson. | ||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Please note that we expect simple scripting skills (e.g. in Python), as students will apply their theoretical knowledge by implementing a machine learning application with given open-source packages. | ||||||||||||||||||||||||||||||||
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| 441-1000-00L | Introduction to Machine Learning in Finance and Insurance  | 4 credits | 3G | B. J. Bergmann, P. Cheridito, A. Ferrario, J. Teichmann | |||||||||||||||||||||||||||||
| Abstract | Provides you with a comprehensive introduction to the fundamentals of machine learning, including key concepts, algorithms, and practical applications. | ||||||||||||||||||||||||||||||||
| Learning objective | You will gain a solid foundation in machine learning and develop the skills to build and evaluate machine learning models for various tasks in the following blocks and modules for the CAS ETH in Machine Learning in Finance and Insurance | ||||||||||||||||||||||||||||||||
| Content | Introduction to Machine Learning with cases. | ||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Only for students of the CAS ETH in Machine Learning in Finance and Insurance | ||||||||||||||||||||||||||||||||
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| 441-1001-00L | Ethics of AI | 2 credits | 1.5S | B. J. Bergmann, A. Ferrario, J. Teichmann | |||||||||||||||||||||||||||||
| Abstract | Provides you with a comprehensive understanding of the ethical dimensions and challenges around machine learning applications in a business and societal context. | ||||||||||||||||||||||||||||||||
| Learning objective | During this course we will reflect on the integration of machine learning which raises profound ethical questions about trust, explainability, accountability, and the regulations that support the use of machine learning empowered technology in different applications. | ||||||||||||||||||||||||||||||||
| Content | Structured as an interactive workshop with guest speakers from academia and industry. | ||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Only open for students of the CAS ETH in Machine Learning in Finance and Insurance. | ||||||||||||||||||||||||||||||||
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| 441-2002-00L | Cases in Machine Learning in Insurance | 2 credits | 2S | B. J. Bergmann, P. Cheridito, A. Ferrario | |||||||||||||||||||||||||||||
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