Moreover, the number of machine learning applications and the volumes of data increase permanently. Nevertheless, the capacities of processing systems, human supervisors, or domain experts remain limited in real-world applications. Furthermore, many applications require an early availability of predictive models, which then have to be refined as the data volume increases. Due to these requirements, approaches that optimise the whole learning process are needed, including the interaction with human supervisors, processing systems, and data of various kinds and at different points in time: techniques for estimating the impact of additional resources (e.g.~data) on the learning progress; methods for the active selection of the information that is processed or queried; techniques for reusing knowledge across time, domains, or tasks, by identifying similarities and adaptation to changes between them; methods for making use of different types of information, such as labelled or unlabelled data, constraints, or domain knowledge. Such techniques are studied, for example, in the fields of adaptive, active, semi-supervised, and transfer learning -- mostly in separate lines of research. Combinations that are capable of operating under various constraints, and thereby address the inherent real-world challenges of volume, velocity, and variability of data and data mining systems, are rarely reported.

Therefore, this combination of a workshop and tutorial will continue to bring together researchers and practitioners from these different areas, thereby stimulating research in interactive and adaptive machine learning systems as a whole. The event continues a successful series of workshops and tutorials at ECML-PKDD 2017 in Skopje (Workshop \& Tutorial), IJCNN 2018 in Rio (Tutorial), ECML-PKDD 2018 in Dublin (Workshop), ECML-PKDD 2019 in Würzburg (Workshop \& Tutorial), ECML-PKDD 2020 (hosted in Ghent, online Workshop), ECML-PKDD 2021 (hosted in Bilbao, online Workshop), and ECML-PKDD 2022 in Grenoble (Workshop).

This workshop evolves around techniques and approaches for optimising the whole learning process, including the interaction with human supervisors, processing systems, and includes adaptive, active, semi-supervised, and transfer learning techniques, and combinations thereof in interactive and adaptive machine learning systems. Our objective is to bridge the communities researching and developing these techniques and systems in machine learning and data mining. Therefore, we welcome contributions that present a new 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.

In particular, we welcome contributions that address aspects including, but not limited to:

Novel Techniques for Active, Semi-Supervised, Transfer, or Weakly Supervised Learning
• methods for big, evolving, or streaming data
• methods for recent complex model structures such as deep learning neural networks or recurrent neural networks
• methods for interacting with imperfect or multiple oracles, e.g., learning from crowds
• methods for incorporating domain knowledge and constraints
• methods for timing the interaction and for combining different types of information
• online and ensemble methods for evolving models and systems, with specific switching and fusion techniques, and (inter-)active data integration techniques
Innovative Use and Applications of Active, Semi-Supervised, Transfer, or Weakly Supervised Learning
• for filtering, forgetting, resampling
• for active class or feature selection, e.g., from multi-modal data
• for detection of change, outliers, frauds, or attacks
• new interactive learning protocols and application scenarios, e.g., brain-computer interfaces, crowdsourcing, ...
• in application in data-intensive science
• in applications with real-world deployment
Techniques for Combined Interactive Adaptive Learning
• methods combining adaptive, active, semi-supervised, or transfer learning techniques
• cost-aware methods and methods for estimating the impact of employing additional resources, such as data or processing capacities, on the learning progress
• methodologies for the evaluation of such techniques and for comparative studies
• methods for automating the control of an interactive adaptive learning process.