Technische Universität München

Fakultät für Informatik

Chair VII Foundations of Software Reliability and Theoretical Computer Science

**Module:** IN2030

**Credits:** ECTS 3.0

**Time:** Monday 8.30 - 10.00 (2 SWS)

**Format:** Moodle Course

**Start:** November 2, 2020

**Content:**

The information in the world doubles every 20 months. Important data sources are business and industrial processes, text and structured data bases, image and biomedical data. Many applications show that data analytics can provide huge benefits. We need models and algorithms to collect, preprocess, analyze, and evaluate data, from various fields such as statistics, system theory, machine learning, pattern recognition, or computational intelligence. In this course you will learn about the most important methods and algorithms for data analytics. You will be able to choose appropriate methods for specific tasks and apply these in your own data analytics projects. You will understand the basic concepts of the growing field of data analytics, which will allow you to keep pace and to actively contribute to the advancement of the field.

- data sources, characteristics, and errors
- data preprocessing and filtering
- data visualization
- data projections (principal component analysis, multidimensional scaling, Sammon mapping, auto associator)
- data transformation and feature selection
- correlation and regression
- forecasting
- classification (Bayes, discriminance, support vector machine, nearest neighbor, learning vector quantization, decision trees)
- clustering (sequential, protype based, fuzzy, relational, heuristic)

**Offered for the following study programs:**
Data Engineering and Analytics, Informatics, Games Engineering

For students of Information Systems and Management we recommend the course Business Analytics (IN2028) which will cover similar content.

For students of Robotics, Cognition, Intelligence the corresponding relevant course content will be covered by the mandatory course Machine Learning (IN2064).

**Prerequisites:**
basic math

**written exam at the end of the semester**

**Literature**

- T. A. Runkler: Data Analytics - Models and Algorithms for Intelligent Data Analysis, 3rd edition, Springer 2020.
- N. Tan, M. Steinbach, V. Kumar: Introduction to Data Mining. Addison Wesley, 2nd edition 2018.
- C. C. Aggarwal: Data Mining: The Textbook. Springer 2015.
- I. H. Witten, E. Frank, M. A. Hall, C. J. Pal: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 4th edition 2016.