Intelligent Systems

Autonomous Learning

Lecture (infAuLearn-01a)

2 SWS,  ECTS credits: 8
suitable for ERASMUS / exchange students, language of instruction: English
Time and place: Monday 14:15 - 15:45 p.m., Ludwig-Meyn-Str. 2, room Ü3
Summer term 2020


Exercise (EinfAuLearn-01a)

Work sheets containing questions, mathematical tasks, and programming tasks (based on provided Jupyter notebooks)
suitable for ERASMUS / exchange students, language of instruction: English
Time and place: Monday 16:15 - 17:45 p.m., Ludwig-Meyn-Str. 2, room Ü3
Summer term 2020


Practical Exercise (PEinfAuLearn-01a)

Mandatory programming tasks in a given computer game environment, i.e. application of Reinforcement Learning algorithms to specific problems (to be accomplished by teams of students). There will be approx. four different practical tasks that need to be finished successfully to be allowed to participate in the exam.
suitable for ERASMUS / exchange students, language of instruction: English
Time and place: Wednesday 10:15 - 11:45 p.m., Ludwig-Meyn-Str. 2, room Ü1
Summer term 2020



Autonomous Learning research aims at understanding how autonomous systems can efficiently learn from the interaction with the environment, especially by having an integrated approach to decision making and learning, allowing systems to autonomously decide on actions, representations, hyper-parameters and model structures for the purpose of efficient learning. The term "autonomous" refers to the ability of the system to learn without or with only very limited external support, which includes manual intervention of humans, availability of pre-defined models or expert knowledge, and availability of large sets of sample data. Specific research topics are: Adaptation of the learning models / techniques based on observations, learning from interaction with the environment, re-using knowledge from one domain in another domain, detection of behaviour that deviates from 'usual' or expected behaviour, and learning from and with other systems of the same kind. The lecture gives an introduction to the field of autonomous learning with a particular focus on a utilisation of the different techniques within intelligent systems. Autonomous Learning is cutting edge research, which means that parts of the lecture are based on current research articles rather than on textbooks. Furthermore, a practitioner's perspective is combined with theoretical understanding of the concepts: the lecture units are combined with traditional exercises but also with practical tasks that have to be solved by making use of techniques discussed in the lecture.


Learning goals

The overall goal of the course is to derive a basic understanding of the motivation, the general concept, and particularly important methods covering the most prominent parts of the field of autonomous learning. This includes techniques for the following aspects of machine learning:

  • Fully autonomous learning behaviour: hyper-parameter optimisation, transfer learning, (self-)evaluation,
  • self-awareness or environment-awareness with a major focus on anomaly/novelty detection
  • By interaction with the environment via sensors and actuators: reinforcement learning
  • By efficiently integrating humans into the learning process: active learning
  • By interacting with other intelligent systems: collaborative learning
  • By using all the above: meta-learning

Particular goals are:

a) Knowledge/Skills:

Understanding of methods for achieving "intelligence" in technical systems, control of learning behaviour with minimal user interaction, continuous self-improvement of system behaviour, cooperation in learning between distributed technical systems

b) Abilities:

Selection and application of techniques of machine learning in technical systems under real-world conditions to control autonomous system behaviour

c) Competencies:

Ability to analyse autonomous learning processes and their behaviour, to determine and interpret relevant assessment parameters / Competence to plan, design and develop intelligent technical systems with the ability to learn autonomously


Basic literature

  • Thomas Mitchell: Machine Learning, The McGraw-Hill Companies, 1997, ISBN 978-0071154673
  • Ethem Alpaydin: Introduction to Machine Learning (Adaptive Computation and Machine Learning). The Mit Press, 3rd revised edition, 2014. ISBN: 978-0262028189
  • C. Müller-Schloer, S. Tomforde: Organic Computing - Technical Systems for Survival in the real World
  • B. Settles: Active Learning
  • M. Yamada, Jianhuii Chen, Yi Chang: Transfer Learning: Algorithms and Applications
  • C. Bishop: Pattern Recognition and Machine Learning (Information Science and Statistics)
  • Sutton, Richard S., and Andrew G. Barto. Introduction to reinforcement learning. Vol. 135. Cambridge: MIT press, 1998.
  • S. Russell and P. Norvig: Künstliche Intelligenz. Ein moderner Ansatz. 3. Aufl. PEARSON



30 h lecture, 30 h classroom exercise, 30 h practical exercise, 120 h self-study

Bachelor Seminar - Self-organised Systems

Seminar (infBSemIS-01a)

2 SWS, ECTS credits: 4
Language of instruction: German
Summer term 2020



The seminar is dedicated to the topic of "Self-organised Systems". In the context of this seminar, this is understood to mean sets of autonomous and technical units that work together to achieve a common goal. Different questions arise that have to be mastered.

The seminar in the winter semester 2019/20 is dedicated to the consistent, fast and reliable storage and the associated finding of content within such self-organized systems. This is examined using algorithms from peer-to-peer computing.

The seminar in summer term 2020 is dedicated to the cooperation of autonomous intelligent systems and the associated interaction patterns within such self-organized systems. This is examined using examples from autonomic and organic computing.


Learning goals

The students learn to independently acquire complex issues using the compact presentation, as is customary in scientific publications (i.e. papers, articles or specialist books) and to prepare them in an understandable form.



30 hours of participation in the seminar, 90 hours of self-study


Master Project - Intelligent Systems

Exercise  (infMPInS-01a)

4 SWS, ECTS-Credits: 10
suitable for ERASMUS / exchange students, language of instruction: English
Time and place: by arrangement

Turtle Bot


This project is offered as a master project – a modified variant for bachelor students is possible (please ask)!



This project is offered as a group task; a team of 3 to 6 students is required (deliverables are adjusted based on a number of participants).



The project is available in English and German, depending on the participants.

Motivation: Robots are a perfect testbed for intelligent systems technology – and autonomous learning techniques in particular. Their mobility, their heterogeneity, and their different application areas define a variety of use cases where learning capabilities are required to initially solve and afterwards improve certain tasks.



What is your task as a group?

  • Set up the basic robot operating system for the robots, assemble robots (i.e., attach sensors, access sensor information, process sensor information).
  1.  We have the turtle bot 3 platform available at our group, which is used for this module. This includes the “TurtleBot3 Waffle Pi” and the “TurtleBot3 Burger” platforms with additional sensor/actuator equipment.
  2. There are a very popular framework and API available that need to be adapted for the scope of this project (framework and API come with comprehensive documentation, tutorials, and community).
  3. The API / framework are available in Python and C++, experimental libraries are also available in JAVA. The programming language is subject to negotiations.
  • Design and implement a framework for autonomous robot behaviour:
  1. Works on heterogeneous robots (i.e., waffle and burger bots with varying sensor and actuator equipment)
  2. Is modular in a way that novel intelligent behaviours can easily be added (and switched on/off)
  3. Follows the design concepts for intelligent systems and provides basic functionality for robot operation tasks
  4. Provides continuous logging of system states, environmental conditions, and actions taken for analysis purposes
  5. Consists of a variable set of autonomous robots and a “base station” with which the robots communicate and share their experiences


  • Use case: classification and novelty detection of environmental conditions
  1. Realise (hardware) scenarios for novel behaviour
    a) Here: environmental behaviour is modelled as different flooring/surface conditions
    b) Set up courses with changing conditions (carpet, stones, tiles, etc)
  2. Classification and novelty detection techniques
    a) Implement classification of floor conditions based on sensor information representations
    b) Implement techniques for novelty detection (abnormal behaviour) that generate an alarm in case of inappropriate classification (i.e., unknown floor)
    c) Apply techniques online and classify floor conditions with different robots
    d) Evaluate the success of the classification


Fundamentals of the module

  • Besides the actual development of the software, the module aims at practical experiences in the following aspects:
    - Specification and definition of “product” using standard software engineering tools
    - (Self-)Organisation as a team, management of the process, deadline supervision
    - Entire software development process until delivery to the customer
    - Documentation of the product
  • Your supervisors are continuously monitoring the process in the role of a customer, i.e. you are expected to regularly demonstrate the progress.


Interested students may contact the group (i.e., Prof. Tomforde at for more details.

There will be an initial appointment begin of the summer term, where the project is set up and students are registered for the module.