| Status | Title | Date | Time | Location | Materials | |
|---|---|---|---|---|---|---|
| 🟢 Completed | Lecture | Session 1: Big Data | 2026-09-04 | 10:00–11:30 | S2.11 (EDV) | Slides |
| 🟢 Completed | Exercise | Session 1: R Setup, Quarto, and Basic Data Workflow | 2026-09-04 | 11:45–13:15 | S2.11 (EDV) | Notebook |
| 🟢 Completed | Lecture | Session 2: Analytics and Data Preparation | 2026-09-04 | 14:15–15:45 | S2.11 (EDV) | Slides |
| 🟢 Completed | Exercise | Session 2: Data Preparation | 2026-09-04 | 16:00–17:30 | S2.11 (EDV) | Notebook |
| 🟢 Completed | Lecture | Session 3: Exploratory Data Analysis | 2026-09-11 | 10:00–11:30 | S2.11 (EDV) | Slides |
| 🟢 Completed | Exercise | Session 3: Exploratory Data Analysis | 2026-09-11 | 11:45–13:15 | S2.11 (EDV) | Notebook |
| 🟢 Completed | Lecture | Session 4: Machine Learning Baselines: Regression | 2026-09-11 | 14:15–15:45 | S2.11 (EDV) | Slides |
| 🟢 Completed | Exercise | Session 4: Machine Learning Baselines: Regression | 2026-09-11 | 16:00–17:30 | S2.11 (EDV) | Notebook |
| 🟢 Completed | Lecture | Session 5: Machine Learning Baselines: Classification | 2026-09-17 | 10:00–11:30 | S2.07 (EDV) | Slides |
| 🟢 Completed | Exercise | Session 5: Machine Learning Baselines: Classification | 2026-09-17 | 11:45–13:15 | S2.07 (EDV) | Notebook |
| 🟢 Completed | Lecture | Session 6: Machine Learning Foundations: Workflow | 2026-09-18 | 10:00–11:30 | S2.07 (EDV) | Slides |
| 🟢 Completed | Exercise | Session 6: Machine Learning Foundations: Workflow | 2026-09-18 | 11:45–13:15 | S2.07 (EDV) | Notebook |
| 🟢 Completed | Lecture | Session 7: Machine Learning Foundations: Algorithms | 2026-09-25 | 10:00–11:30 | S2.07 (EDV) | Slides |
| 🟢 Completed | Exercise | Session 7: Machine Learning Foundations: Algorithms | 2026-09-25 | 11:45–13:15 | S2.07 (EDV) | Notebook |
| 🟢 Completed | Lecture | Session 8: Machine Learning for Non-Tabular Data | 2026-09-25 | 14:15–15:45 | S2.07 (EDV) | Slides |
| 🟢 Completed | Exercise | Session 8: Machine Learning for Non-Tabular Data | 2026-09-25 | 16:00–17:30 | S2.07 (EDV) | Notebook |
| 🟢 Completed | Lecture | Session 9: Deployment in Organizations | 2026-10-02 | 10:00–11:30 | S2.07 (EDV) | Slides |
| 🟢 Completed | Exercise | Session 9: Deployment in Organizations: Explainable AI | 2026-10-02 | 11:45–13:15 | S2.07 (EDV) | Notebook |
| 🟢 Completed | Group presentation | Session 10: Group Work Presentations | 2026-10-02 | 14:15–15:45 | S2.07 (EDV) | Slides |
| 🟢 Completed | Group presentation | Session 10: Group Work Presentations | 2026-10-02 | 16:00–17:30 | S2.07 (EDV) | Slides |
| 🟢 Completed | Group presentation | Session 10: Group Work Presentations | 2026-10-09 | 10:00–11:30 | S2.07 (EDV) | Slides |
| 🟢 Completed | Group presentation | Session 10: Group Work Presentations | 2026-10-09 | 11:45–13:15 | S2.07 (EDV) | Slides |
Machine Learning for Big Data
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Instructor: Prof. Dr. Gerit Wagner
Welcome to Machine Learning for Big Data. This course introduces machine learning as a core part of data science for big data contexts, where analysts work with large, high-dimensional, and diverse datasets. Throughout the semester, you will learn how modern machine learning methods complement classical statistics, how to prepare and explore data, and how to build models for classification, prediction, and managerial decision support. The course combines lectures, in-class and home exercises, and practical work with R to help you connect methods and algorithms to real analytical problems.