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Machine Learning for Big Data

Course logistics

Session 0

Welcome

Prof. Dr. Gerit Wagner

Academic background

  • 2009-2018: Universität Regensburg (Bachelor/Master/Doctoral Student)
  • 2014: University of Oulu (ERASMUS)
  • 2019-2022: HEC Montréal (Postdoctoral Fellowship)
  • 2022-2026: Otto-Friedrich-Universität Bamberg (Assistant Professor)
  • Since 2026: Frankfurt School of Finance & Management (Full Professor)

My teaching and research interests

  • Programming (on GitHub)
  • AI for knowledge work, health, and literature reviews

Things I do outside of work

  • Running and working out in the gym
  • Hiking, skiing, sailing with friends
  • Contribute to OpenStreetMap using the StreetComplete app

About you

Please take a moment to think about the following questions. For each question, I’ll invite a few of you to briefly share your thoughts.


1. Have you worked in an industry role or completed an internship with an analytics focus?

  • What was something that felt very useful in your analytics work?
  • Or where would you have liked to be better prepared for a data-related task?

2. Which analytics tools or programming languages have you used?

  • What was a particularly interesting analytics challenge you encountered?
  • Or a tool you found especially powerful or surprising?

3. What are your expectations for the course?


Please use your name tags.

Machine Learning for Big Data

Machine Learning for Big Data in the curriculum

Course architecture

What this course does not cover

  • Engineering of production-grade analytical systems

    • Software engineering
    • Database design
    • DevOps/MLOps pipelines
    • Scalable application development

  • Statistical theory

    • Inferential statistics
    • Proofs/derivations

  • Mathematical optimization and simulation models

    • Scheduling, logistics, queueing

  • Autonomous or agentic decision systems

    • Robots/agents acting on decisions
    • Reinforcement learning

We will touch these areas where needed, but our focus is on analytical reasoning for business decisions
and R-based analysis in Quarto documents.

Group work brief

Read the group work brief, which is linked on the Canvas course page.

Check the modalities and dates
Do they work for you?

Take notes on questions or unclear points

We will discuss your questions together afterwards.

Course logistics

Course logistics

Workload

  • 150 h total
  • 44 academic teaching hours (45 minutes each)
  • Remaining workload: self-study, including lesson preparation and follow-up, reading, assessment preparation, and take-home assignments

Assessment

  • Group project: ongoing, submitted as a text document at the end of the module
  • 120 performance points

Contact

Individual circumstances

If you have religious holidays, family responsibilities, health-related matters, or other individual circumstances that may affect your participation or performance, please reach out early. We will work together to find a fair and workable solution.

Materials

Slides and materials

  • Presentation slides and documents will be made available for download.

Short surveys at the end of each session

  • If you notice anything that could benefit from further clarification or improvement, please take a note and let me know.

Your input makes a real difference 🙏


Key concept markers

These markers identify slides that cover concepts, methods, or skills that are particularly important for the course. They help distinguish core material from supporting examples and illustrations.


Note: Other content still contributes to a complete understanding.

Literature

  • You are expected to take complementary notes and read the recommended literature.
  • Literature and complementary materials will be listed at the end of each lecture.
  • Reading of complementary materials depends on your interest and ambition.

General Introduction

Methods and Algorithms

  • Alpaydin (2016a)
  • Hastie, Tibshirani, and Friedman (2009)
  • James et al. (2013)

R and Data Analysis

  • Wickham, Çetinkaya-Rundel, and Grolemund (2023)

Changelog

Changes implemented for this semester:

  • Projects: open topic (building on the CONTRIBUTE framework (Thurner and Wagner 2026))
  • Tidyverse instead of Base R
  • Quarto reports
  • Transparent session structure

You may also be interested in …

Master’s theses: See SuSy and additional information on this page.

In SuSy, you can find more information on my research topics:

  • Open, Agentic, and Boundary-Spanning Work
    This area examines how AI agents and open digital infrastructures reshape knowledge work and collaboration across organizational boundaries. It focuses on agentic systems embedded in real work contexts (e.g., Git workflows, handbooks, and repositories) and how they transform coordination, governance, and accountability.
  • AI-Supported Knowledge Synthesis
    This area investigates how AI supports knowledge synthesis across academic and professional contexts, focusing on transparency, rigor, and traceability in AI-assisted processes. It includes literature reviews and synthesis in digital gardens, second-brain systems, and other knowledge repositories.





References

Alpaydin, Ethem. 2016a. Introduction to Machine Learning. 3rd ed. MIT Press.
Alpaydin, Ethem. 2016b. Machine Learning: The New AI. MIT Press Essential Knowledge Series. MIT Press.
Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. Springer. https://doi.org/10.1007/978-0-387-84858-7.
James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning: With Applications in R. Springer. https://doi.org/10.1007/978-1-4614-7138-7.
Schmarzo, Bill. 2016. Big Data MBA: Driving Business Strategies with Data Science. Wiley.
Schutt, Rachel, and Cathy O’Neil. 2013. Doing Data Science: Straight Talk from the Frontline. O’Reilly Media.
Thurner, Laureen, and Gerit Wagner. 2026. “CONTRIBUTE: A Pedagogical Framework for Open Source Software Development Projects.” Communications of the Association for Information Systems 59 (1). https://aisel.aisnet.org/cais/vol59/iss1/32/.
Wickham, Hadley, Mine Çetinkaya-Rundel, and Garrett Grolemund. 2023. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. 2nd ed. O’Reilly Media. https://r4ds.hadley.nz/.