Autonomous Learning

Lecture (infAuLearn-01a)

2 SWS, ECTS credits: 8
Suitable for ERASMUS / exchange students, language of instruction: English
Time and place: Tuesday 14:15 - 15:45, online
Lecture period sommer term 2021: Tuesday, April 20th, 2021 - Friday, July 9th, 2021
For more information UnivIS

Exercise (EinfAuLearn-01a)

Work sheets containing questions, mathematical tasks, and programming tasks (based on provided Jupyter notebooks)
2 SWS
suitable for ERASMUS / exchange students, language of instruction: English
Time and place: Tuesday 08:30 - 10:00, online
Lecture period sommer term 2021: Tuesday, April 27th, 2021 - Friday, July 9th, 2021
 

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.
2 SWS
suitable for ERASMUS / exchange students, language of instruction: English
Time and place: Wednesday 08:30 - 10:00, online
Lecture period sommer term 2021: Wednesday, April 21th, 2021 - Friday, July 9th, 2021

Summary

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.

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

Workload

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