LV 706.315 Interactive Machine Learning (iML)

LV 706.315 Selected Topics on interactive Knowledge Discovery:
interactive Machine Learning (iML) – Winter term 2015 (again in WS 2016)

LV-706315-interactive-machine-learning-holzinger

Motivation for this lecture:

Whilst automated Machine Learning (aML) approaches (“Google car“) work well in many domains, particularly with big data sets, in complex domains with small training data sets the application of aML entails the danger of modelling artefacts.  An example for a complex domain is Biomedicine, where experts are often confronted with high-dimensional, probabilistic, incomplete and often small data sets,  which makes the application of aML-approaches difficult. In such situations it can be advantageous to integrate human domain knowledge and expertise into the machine learning loop. The foundations of iML-approaches can be found in reinforcement learning, preference learning and active learning.

Definition of interactive Machine Learning (iML):

We define iML-approaches as algorithms that can interact with both computational and human agents *) and can optimize its learning behaviour trough this interaction.

*) Such agents are called in Active Learning “oracles” (see e.g.: Settles, B. 2011. From theories to queries: Active learning in practice. In: Guyon, I., Cawley, G., Dror, G., Lemaire, V. & Statnikov, A. (eds.) Active Learning and Experimental Design Workshop 2010. Sardinia: JMLR Proceedings. 1-18.

Goal of this lecture:

This graduate course follows a research-based teaching (RBT) approach and provides a broad overview of models and discusses methods for combining human intelligence with machine intelligence to solve computational problems. The application focus is on the biomedical domain. The cross-sectional topic is evaluation, because the all-in-one method suitable for every purpose (in German: eierlegende Wollmilchsau) would be nice but is not achievable. Consequently, an evaluation framework is of extreme importance for driving progress in any machine learning approach. For practical applications we use the JULIA language – besides of R and Python.

Background:

A classic challenge in machine learning is in the development of a model, which relates observed data X ܺto categorical variables Y  ∈ {x1, x2, x3, … } to infer higher level information from the data.  This problem is widely applicable as X may represent any data and Y any information. In domains dealing with uncertainties (such as the biomedical domain), we seek applications where Y includes unknowns. Machine learning solutions to this problem must involve human expertise during the design phase to provide relevant training data sets. Unfortunately, to date such experts are not part of machine learning algorithms, on the contrary, the grand goal of artificial intelligence is to exclude the human from the loop, hence make it fully automatic (see “Google car”). The objective of interactive machine learning methods is to develop algorithms which can interact with both: computational agents and human agents – towards hybrid multi-agent systems.

General information:

Machine learning is a large subfield of computer science that evolved from artificial intelligence (AI) and is tightly connected with data mining and knowledge discovery. The grand goal is to design and develop algorithms which can learn from data. Consequently, machine learning systems learn and improve with experience and time and can be used to predict outcomes of questions based on previous knowledge. Actually, to learn intelligent behaviour from noisy examples is a grand exciting challenge in AI. This is highly relevant for many applications in biomedical informatics generally, and for stratified and personalized medicine in particular.

Target Group:

Research students of Computer Science who are interested in knowledge discovery/data mining by following the idea of iML-approaches, i.e. human-in-the-loop learning systems. This is a cross-disciplinary computer science topic and highly relevant for the application in complex domains, such as biomedicine.

Keywords:

Interactive Machine Learning, Reinforcement Learning, Preference Learning, Active Learning, Computational Intelligence