infAuLearn-01a: Autonomous Learning (infAuLearn-01a) (080037)- Dozentinnen/Dozenten
- Prof. Dr.-Ing. Sven Tomforde, Connor Schönberner, M.Sc., Nikita Smirnov, M.Sc.
- Angaben
- Vorlesung, 2 SWS, ECTS-Studium, ECTS-Credits: 8
Praesenzveranstaltung, für ERASMUS-/Austauschstudierende geeignet, Unterrichtssprache Englisch
Zeit und Ort: Mi 10:15 - 11:45, LMS8 - R.EG.010 (26)
vom 9.4.2023 bis zum 9.7.2023
- Voraussetzungen / Organisatorisches
- 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
Other requirements:
No mandatory modules. However, basic modules such as mathematics 1 to 3 are considered to be common knowledge. We recommend to visit the "Intelligent Systems" module before.
Exam Performance:
Oral exam (duration: 25 minutes) or written exam (90 minutes) - depending on COVID19 developments and number of participants. Successful completion of all practical tasks (i.e., second part / practical part of the exercises) plus quizzes is mandatory for participation in the exam. Practical tasks and quizzes are not graded, only passed/not passed.
- Inhalt
- a) Introduction and organisation
b) Recapitulation of basic machine learning terms and techniques
c) Model selection (hyper-parameter optimisation and evaluation)
d) Transfer Learning
e) Self-Awareness
f) Reinforcement Learning
g) Active Learning
h) Collaborative Learning
i) Summary and outlook (incl. meta-learning)
Teaching and Learning Methods:
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. In addition, a second type of exercise is used where
a) 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
b) The exercise uses the following building blocks:
Exercise sheets with written tasks (e.g. calculations)
Implementation of individual tasks (mostly in Python using Jupyter Notebooks)
Reading and assessing scientific publications (i.e. journal articles or conference papers)
Presentation of the results by participants
Discussions
c) The practical exercise tasks use the following building blocks: - Practical tasks (e.g., given in Jupyter Notebooks) need to be solved by actually applying, testing, and analysing the behaviour of techniques from the field of autonomous learning - In particular, 4 to 6 assignments allowing for an increasing complexity in '0self-learning systems' have to be solved by the students.
- Empfohlene Literatur
- 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
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 25
- Zugeordnete Lehrveranstaltungen
- UE: Exercise: Autonomous Learning (080003)
-
Dozentinnen/Dozenten: Prof. Dr.-Ing. Sven Tomforde, Connor Schönberner, M.Sc., Nikita Smirnov, M.Sc.
Zeit und Ort: Mo 14:15 - 15:45, LMS8 - R.EG.010 (26)
- UE: Practical Exercise: Autonomous Learning (080045)
-
Dozentinnen/Dozenten: Prof. Dr.-Ing. Sven Tomforde, Connor Schönberner, M.Sc., Nikita Smirnov, M.Sc.
Zeit und Ort: Mi 12:15 - 13:45, LMS8 - R.EG.010 (26)
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