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<pre>
                                  Preface


Science, technology, and commerce increasingly recognise the importance of ma-
chine learning approaches for data-intensive, evidence-based decision making.
This is accompanied by increasing numbers of machine learning applications
and volumes of data. Nevertheless, the capacities of processing systems or hu-
man supervisors or domain experts remain limited in real-world applications.
Furthermore, many applications require fast reaction to new situations, which
means that first predictive models need to be available even if little data is
yet available. Therefore approaches are needed that optimise the whole learning
process, including the interaction with human supervisors, processing systems,
and data of various kind and at different timings: techniques for estimating the
impact of additional resources (e.g. data) on the learning progress; techniques
for the active selection of the information processed or queried; techniques for
reusing knowledge across time, domains, or tasks, by identifying similarities and
adaptation to changes between them; techniques for making use of different types
of information, such as labeled or unlabeled data, constraints or domain knowl-
edge. Such techniques are studied for example in the fields of adaptive, active,
semi-supervised, and transfer learning. However, this is mostly done in separate
lines of research, while combinations thereof in interactive and adaptive ma-
chine learning systems that are capable of operating under various constraints,
and thereby address the immanent real-world challenges of volume, velocity and
variability of data and data mining systems, are rarely reported. Therefore, this
workshop aims to bring together researchers and practitioners from these differ-
ent areas, and to stimulate research in interactive and adaptive machine learning
systems as a whole. It continues a successful series of events at ECML PKDD
2017 in Skopje (Workshop and Tutorial), IJCNN 2018 in Rio (Tutorial), ECML
PKDD 2018 in Dublin (Workshop), ECML PKDD 2019 in Würzburg (Workshop
and Tutorial), and virtual ECML PKDD 2020 (Workshop).
The workshop aims at discussing techniques and approaches for optimising the
whole learning process, including the interaction with human supervisors, pro-
cessing systems, and includes adaptive, active, semi-supervised, and transfer
learning techniques, and combinations thereof in interactive and adaptive ma-
chine learning systems. Our objective is to bridge the communities researching
and developing these techniques and systems in machine learning and data min-
ing. Therefore, we welcome contributions that present a novel problem setting,
propose a novel approach, or report experience with the practical deployment of
such a system and raise unsolved questions to the research community.
II     Preface

All in all, we accepted 10 papers (13 papers submitted) to be published in these
workshop proceedings. The authors discuss approaches, identify challenges and
gaps between active learning research and meaningful applications, as well as
define new application-relevant research directions. We thank the authors for
their submissions and the program committee for their hard work.

September 2021                 Georg Krempl, Vincent Lemaire, Daniel Kottke
                                        Andreas Holzinger, Barbara Hammer
                      Organization


Organizing Committee
Adrian Calma
Andreas Holzinger      Graz University of Technology
Daniel Kottke          University of Kassel
Georg Krempl           Utrecht University
Vincent Lemaire        Orange Labs France

Program Committee
Michael Beigl          TECO, KIT
Mirko Bunse            Dortmund University
Klemens Böhm          Karlsruhe Institute of Technology
Hudelot Céline        Ecole Centrale Paris
Gregory Ditzler        University of Arizona
Michael Granitzer      University of Passau
Marek Herde            University of Kassel
Martin Holena          Institute of Computer Science
Denis Huseljic         University of Kassel
Edwin Lughofer         Johannes Kepler University Linz
Bernhard Pfahringer    University of Waikato
Stefano Teso           Katholieke Universiteit Leuven
Indre Zliobaite        University of Helsinki

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