## Invited Speakers

**Professor Marie Cottrell, Université Paris 1 Panthéon-Sorbonne **

Theoretical and applied aspects of the self-organizing maps

The SOM is widely used, easy to implement, has nice properties for data mining by providing both clustering and visual representation. It acts as an extension of the k-means algorithm that complies as much as possible with the topological structure of the data.

However, since its origin, the mathematical study is difficult and is completed only in very special cases. In WSOM 2005, Jean-Claude Fort presented the state of the art, the main remaining difficulties and the mathematical tools that can be mobilized: Markov chains, theory of Ordinary Differential Equations, the theory of stability, … In this lecture we try if possible to present the theoretical advances made since then.

In addition, we pass in review some of the many original SOM algorithm variants which were defined to overcome the theoretical difficulties and/or adapt the algorithm to the processing of complex data such as time series, missing values in the data, nominal data, textual data.

**Professor Pablo Estévez, University of Chile and Millennium Institute of Astrophysics, Chile**

Big Data Era Challenges and Opportunities in Astronomy – How SOM/LVQ Related Machine Learning Can Contribute?

Astronomy is facing a paradigm shift caused by the exponential growth of the sample size, data complexity and data generation rates of new sky surveys. For example, the Large Synoptic Survey Telescope (LSST), which will begin operations in northern Chile in 2022, will generate a nearly 150 Petabyte imaging dataset. The LSST is expected not only to improve our understanding of time varying astrophysical objects, but also to reveal a plethora of yet unknown faint and fast-varying phenomena. To cope with a change of paradigm to data-driven astronomy new computational intelligence, machine learning and statistical approaches are needed. In this talk I will present big data era challenges and opportunities in astronomy from the point of view of machine learning. In particular, I will address the question of how SOM/LVQ related methods can contribute to cope with these challenges.

**Professor Risto Miikkulainen, University of Texas at Austin**

Using SOMs to gain insight into human language processing

While SOMs are commonly used as a tool for data visualization and data analysis, they can also serve as a model for cognitive functions in humans. Such functions include semantic and episodic memory, vision, and language. In this talk I will review how elements of sentence meaning can be laid out on a map, resulting in human-like graded semantic understanding instead of a single parse tree. I will also describe a model of the lexicon that can be fit to the individual patient with aphasia, and used to predict optical rehabilitation treatments.