This is accompanied by increasing numbers of machine learning applications and volumes of data. Nevertheless, the capacities of processing systems or human 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 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. However, this is mostly done in separate lines of research, while combinations thereof in interactive and adaptive machine 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 combined tutorial and workshop aims to bring together researchers and practitioners from these different areas, and to stimulate research in interactive and adaptive machine learning systems as a whole.

This workshop aims at discussing 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 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.

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

Novel Techniques for Active, Semi-Supervised, Transfer 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,
Innovative Use and Applications of Active, Semi-Supervised, Transfer 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 comparative studies,
• methods for automating the control of an interactive adaptive learning process.