Machine Learning for Business Analytics

2025/2026

Academic Year

ENG

Instruction in English

3

ECTS credits

Delivered at:

Department of Business Informatics

Course type:

Compulsory course

When:

1 year, 2 module

Instructor

Knoll, Dominik J.

Course Syllabus

Abstract

In an era where data-driven decision-making defines competitive advantage, machine learning has emerged as an indispensable tool for business analytics professionals. This comprehensive course equips students with the knowledge and skills to leverage machine learning algorithms for solving real-world organizational challenges. The course balances conceptual understanding with hands-on implementation, ensuring graduates can both communicate effectively with technical teams and execute end-to-end analytical projects. Students will explore foundational supervised and unsupervised learning techniques, including regression, classification, clustering, and ensemble methods. The curriculum emphasizes the complete machine learning workflow: problem formulation, data preparation, feature engineering, model selection and evaluation, and deployment considerations. Special attention is devoted to interpretability and ethical considerations, recognizing that business stakeholders require transparent, explainable insights rather than black-box predictions. Through case studies drawn from diverse fields including retail, finance, healthcare, and e-commerce, students will develop critical judgment in selecting appropriate methodologies for specific business objectives. By course completion, participants will possess the analytical acumen to identify machine learning opportunities within their organizations, execute projects using industry-standard tools (Python, scikit-learn), evaluate model performance through metrics, and effectively communicate findings to non-technical stakeholders. This course transforms machine learning from an abstract technical domain into a practical capability, empowering alumni to drive evidence-based management and measurable business impact.

Learning Objectives

• Machine learning paradigms - supervised, unsupervised, and reinforcement learning approaches
• Core ML algorithms - regression, classification, clustering, and ensemble methods with their mathematical foundations
• Evaluation and metrics - performance measurement, model selection, and validation techniques
• Business applications - ML use cases across industries and functional domains
• ML project lifecycle - from problem formulation through data preparation to model deployment
• Ethics and governance - bias, fairness, transparency, and regulatory compliance of ML systems

Expected Learning Outcomes

• Explain fundamental machine learning concepts including the distinction between supervised, unsupervised, and reinforcement learning, and articulate when each paradigm is appropriate for business problems.
• Describe the theoretical foundations of key ML algorithms (linear/logistic regression, support vector machines, decision trees, random forests, k-means, etc.) and their underlying mathematical principles and the appropriate evaluation metrics for each algorithm type.
• Identify common business use cases for machine learning across different industries and functional areas (marketing, operations, finance, HR) and evaluate their feasibility and potential impact.
• Understand the machine learning workflow from problem definition through deployment, including data collection, preprocessing, model training, validation, and monitoring.
• Recognize ethical considerations and biases in machine learning systems, including fairness, transparency, privacy concerns, and regulatory compliance.

Course Contents

• Unit 1: Machine Learning Fundamentals & Business Context
• Unit 2: ML Algorithms, Metrics & the ML Workflow
• Unit 3: Model Evaluation, Deployment & Ethics

Assessment Elements

• Seminar Attendance
• In-Class participation and engagement (individual)
  It is not about simple attendance, but evaluates ACTIVE PARTICIPATION in the lectures, with timely and relevant comments and discussion, comments linked to previous lectures, assigned readings (including videos), personal experience, or other courses; opinion based on evidence, thinking, responding to the lecturer’s questions. Students need to present themselves to the instructor to record their active participation.
• Seminar and Homework
  In teams of 3 students formed freely within the same seminar group
• Exam (individual)
  The exam is blocking, meaning a minimum grade of 5 is required to pass the course The exam is taken in a written format (computer based) using closed and open questions.

Interim Assessment

• 2025/2026 2nd module
  0.35 * Exam (individual) + 0.15 * In-Class participation and engagement (individual) + 0.1 * Seminar Attendance + 0.4 * Seminar and Homework

Bibliography

Recommended Core Bibliography

• Aurélien Géron. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems: Vol. Second edition. O’Reilly Media.
• Foundations of machine learning, Mohri, M., 2012

Recommended Additional Bibliography

• Introduction to machine learning, Alpaydin, E., 2020
• Машинное обучение для абсолютных новичков : вводный курс, изложенный простым языком, Теобальд, О., 2024

Authors

• Knoll Dominik Iokhannes
• KOROTKIN BORIS ALEKSANDROVICH
• REDKINA GALINA SERGEEVNA