Introduction to systems engineering and AI processes

Aims

The lecture deals with current problems in information technology and the tools for solving them, from simple algorithms 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 and determine their runtime complexity. They should be able to compare and demonstrate known sorting, search and optimization algorithms. Furthermore, students should be able to classify, describe and evaluate methods of machine learning and also be able to name and differentiate the characteristics, properties and components of self-learning systems. In this context, students will be able to assess approaches to managing large data sets and describe the characteristics and procedures of this analysis.
The lecture goes on to discuss methods of anomaly detection and IT security terms. Students should be able to reproduce and categorize these.

Contents

The focus of the course is on

• Fundamentals and properties of different classes of algorithms
• Self-learning systems and machine learning (clustering, neural networks, etc.)
• Fundamentals and methods for analyzing large data sets
• Processes for handling large data sets
• Methods for anomaly detection as a field of application of self-learning systems on large amounts of data

Literature

• Cormen T.H.; Leiserson C. E.; Riverest R.L.: Algorithmen - Eine Einführung, Oldenburg, 2nd edition (2007), ISBN: 978-3486582628 English version: Introduction to Algorithms, B&T, 2nd edition (2001), ISBN: 978-0262032933
• Pomberger, G.; Dobler, H.: Algorithmen und Datenstrukturen, Eine systematische Einführung in die Programmierung, 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 Practically, mitp; Edition: 1st edition 2017, ISBN: 3958455468
• Géron, A.: Practical introduction to machine learning with Scikit-Learn and TensorFlow: Concepts, tools and techniques for intelligent systems, O'Reilly, ISBN: 3960090617
• Claudia Eckert: IT Security. Concepts - Procedures - Protocols. 7th, revised and expanded edition. Oldenbourg, 2012, ISBN 978-3-486-70687-1