Modulcode:	infInS-01a
Englische Bezeichnung:	Intelligent Systems
Modulverantwortliche(r):	Prof. Dr.-Ing. Sven Tomforde
Turnus:	unregelmäßig (WS19/20 WS20/21 WS21/22 SS22)
Präsenzzeiten:	4V 2Ü
ECTS:	8
Workload:	60 h lectures, 30 h exercises, 150 h self studies
Dauer:	ein Semester
Modulkategorien:	BSc-Inf-WP (BSc Inf (21)) WI (BSc Inf (15)) MSc-Inf-WP (MSc Inf (21)) 2F-MEd-Inf-WP (MEd-Hdl Inf (21)) 2F-MA-Inf-WP (2F-MA Inf (21)) MSc-WInf-WP-Inf (MSc WInf (21)) WI (MSc Inf (15))
Lehrsprache:	Englisch
Voraussetzungen:	Inf-Math-A Inf-Math-B Inf-Math-C infEInf-01a

Kurzfassung:

• First appointment is Wednesday, 4.11. at 12:15
• There the organisation of the event will be discussed and the access to OpenOLAT, Zoom, etc. will be announced
• Zoom Link for this appointment: https://uni-kiel.zoom.us/j/84263947011?pwd=S3g2ajZxaURoTmVaVmpaV3dXMjVqdz09

An "Intelligent system" is a computing system capable of operating under difficult conditions (e.g. time-varying environments, emergent situations or disturbances) by autonomously adapting its behaviour to changing conditions and learning autonomously. The main goal of engineering intelligent systems is to counter the challenges of complexity by means of integrating desired characteristics such as robustness, flexibility or resilience into technical systems. This is combined with a continuous improvement of the system behaviour. The improvement process is achieved by different approaches of machine learning, e.g. from the fields of reinforcing, active or semi-supervised learning. Besides these learning-related aspects, the design and organisation of large-scale intelligent systems consisting of a potentially large group of autonomous subsystems requires techniques for self-organisation as well as mechanisms for trust relations and fairness.

The lecture gives an introduction to the design and realisation of intelligent systems. It is based on the insights of research initiatives such as "Organic Computing" and "Autonomic Computing".

Lernziele:

The overall goal of the course is to derive a basic understanding of the motivation, the general concept, and engineering methods of intelligent systems. Based on this, students will learn about machine learning techniques capable of gathering and describing the environmental and internal conditions of an intelligent systems as well as for improving the behaviour autonomously at runtime.

Particular goals are:

• Students understand the motivation and the need for intelligent systems that act autonomously without (or with only limited) user intervention or guidance.
• Students can define the terms "Intelligent System", "Organic Computing", and "Autonomic Computing"
• Students are able to design intelligent systems by assessing and selecting a suitable basic model.
• Students can implement selected methods for clustering and classifying situations based on data gathered by sensors.
• Students can compare algorithms for learning from feedback and implement the most promising variant.
• Students are capable of quantifying system aspects of large-scale organisations of autonomous intelligent systems with respect to characteristics such as robustness, emergence, self-organisation, autonomy, or adaptivity.

Lehrinhalte:

• Introduction and organisation
• Design of Intelligent Systems
• Pre-processing of signals
• Representation of time series
• Similarity of time series
• Segmentation of time series
• Clustering
• Classification
• Anomaly/Novelty detection
• Evaluation
• Self-organised order
• Quantification of system properties
• Model learning
• Learning from feedback
• Mutual influences
• Planning based on optimisation techniques
• Collaboration schemes

Weitere Voraussetzungen:

No mandatory modules.

However, basic modules (i.e. mathematics and operating systems / communication systems) are considered to be common knowledge.

Prüfungsleistung:

Written exam (90 minutes).

Successful completion of all term-based assignments is mandatory for participation in the exam. These assignments include: three quizzes (to be done via OpenOLAT according to a given deadline) and three practical tasks (applying the techniques from the lecture to a given data set and reaching a defined level of accuracy). Please note that the assingments are not graded, only passed/not passed - they are a prerequisite for the exam.

Lehr- und Lernmethoden:

The lecture is accompanied by an exercise that requires active participation of the students. Task sheets and presence work will be discussed in small groups of students.

The lecture is based on the use of the following teaching methods:

• Explanatory lecture with slide sets
• Blackboard with illustrating examples
• Use of video sequences for illustration purposes
• Interactive elements (questions, tasks)
• Internet-based quiz

The exercise uses the following building blocks:

• Exercise sheets with written tasks (e.g. calculations)
• Implementation of individual tasks (e.g. in JAVA or NetLogo)
• Reading and assessing scientific publications (i.e. journal articles or conference papers)
• Presentation of the results by participants
• Discussions

Verwendbarkeit:

The derived understanding serves as basis for the subsequent course "Autonomous Learning".

Literatur:

Basic literature:

• Duda, Richard O., Peter E. Hart, and David G. Stork: "Pattern classification", John Wiley & Sons, 2012, ISBN: 978-0471056690
• Christian Müller-Schloer and Sven Tomforde: Organic Computing - Technical Systems for Survival in the Real World, Birkhäuser Verlag, Basel, 2018, ISBN 978-3319684765

Further readings:

• Christian Müller-Schloer, Hartmut Schmeck, Theo Ungerer (eds.): Organic Computing - A Paradigm Shift for Complex Systems, Birkhäuser Verlag, Basel, 2011, ISBN 978-3034801294
• Rolf Würtz (ed.): Organic Computing (Understanding Complex Systems), Springer Verlag Berlin, 2008, ISBN 978-3540776567
• Thomas Mitchell: Machine Learning, The McGraw-Hill Companies, 1997, ISBN 978-0071154673
• Philippe Lalanda, Julie McCann, Ada Diaconescu: Autonomic Computing - Principles, Design and Implementation, 2013, Springer Verlag, ISBN 978-1447150060
• Ethem Alpaydin: Introduction to Machine Learning (Adaptive Computation and Machine Learning). The Mit Press, 3rd revised edition, 2014. ISBN: 978-0262028189

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