Technische Universität München
Fakultät für Informatik
Chair VII Foundations of Software Reliability and Theoretical Computer Science

Data Mining und Knowledge Discovery (WS 18/19)

Prof. Dr. Thomas Runkler

Module: IN2030

Credits: ECTS 3.0

Time: Monday 8.30 - 10.00 (2 SWS)

Room: Interims Hörsaal 2 (5620.01.102)

Start: October 22, 2018

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.

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