TUTORIAL 1: Scalable Learning of Graphical Models
Date (Time): 28th March 2018 (9:00 – 12:30)
ABSTRACT: From understanding the structure of data, to classification and topic modelling, graphical models are core tools in machine learning and data mining. They combine probability and graph theories to form a compact representation of probability distributions. In the last decade, as data stores became larger and higher-dimensional, traditional algorithms for learning graphical models from data, with their lack of scalability, became less and less usable, thus directly decreasing the potential benefits of this core technology. To scale graphical modelling techniques to the size and dimensionality of most modern data stores, data science researchers and practitioners now have to meld the most recent advances in numerous specialised fields including graph theory, statistics, pattern mining and graphical modelling. This tutorial will cover the core building blocks that are necessary to build and use scalable graphical modelling technologies on large and high-dimensional data.
Instructor: Prof. Dr. Geoff Webb
Center of Data Science, Monash University, Australia
TUTORIAL 2: Best Practices in Offline and Online Experimentation and the Role of Click Data in Testing
Date (Time): 28th March 2018 (14:00 – 17:30)
ABSTRACT: Prof. Sanderson recently attended a number of tutorials and keynote talks that taught him much about how commercial search engines innovate and improve. The key lies in creating evaluation infrastructures that allow search and interface changes to be tested quickly and accurately. In this talk he will give an overview of the talks he attended, highlight key points, and provide pointers to the slides from those talks.
Instructor: Prof. Dr. Mark Sanderson
School of Computer Science and Information Technology, RMIT University, Melbourne, Australia