Information technology II and automation technology

Objectives

The lecture deals with current problems in information technology and the tools for their solution, starting from simple algorithms up to self-learning systems and the processes for handling Big Data problems. At the end of the lecture, students should be able to classify the characteristics, properties and classes of algorithms, as well as determine their runtime complexity. Known sorting, search and optimization algorithms should be compared and demonstrated. Furthermore, the students should be able to classify, describe and evaluate methods of machine learning and furthermore be able to name and delimit the characteristics, properties and components of self-learning systems. In this context, students will be able to assess approaches for managing large data sets and describe the characteristics, as well as the procedure of this analysis.
In the further course the lecture deals with methods of anomaly detection and terms of IT security. Students should be able to reproduce and classify these.

Contents

The main topics of the course are:

• Basics and properties of different classes of algorithms
• Self-learning systems and machine learning (clustering, neural networks, etc.)
• Basics and processes for analyzing large data sets
• Processes for handling large data sets
• Anomaly detection processes as a field of application of self-learning systems to large data sets

Literature

• Cormen T.H.; Leiserson C. E.; Riverest R.L.: Algorithms - An Introduction, Oldenburg, 2nd Edition (2007), ISBN: 978-3486582628 English Version: Introduction to Algorithms, B&T, 2nd Edition (2001), ISBN: 978-0262032933
• Pomberger, G.; Dobler, H.: Algorithms and Data Structures, A Systematic Introduction to Programming, PEARSON Studium Verlag, 2008, ISBN: 978-3-8273-7268-0
• Sedgewick, R.: Algorithms, ADDISON-WESLEY - PEARSON Studium Verlag, 2nd edition (2003), ISBN: 3-8273-7032-9
• Provost, F.; Fawcett, T.: Data Science for Business: Applying data mining and data analytic thinking in practice, mitp; edition: 1st ed. 2017, ISBN: 3958455468.
• Géron, A.: Praxiseinstieg Machine Learning mit Scikit-Learn und TensorFlow: Konzepte, Tools und Techniken für intelligente Systeme, O'Reilly, ISBN: 3960090617.
• Claudia Eckert: IT Security. Concepts - Procedures - Protocols. 7th, revised and expanded edition. Oldenbourg, 2012, ISBN 978-3-486-70687-1.