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Data Science Master Track Tom Heskes and Niklas Weber Scientific questions you will study • What is clustering? • What is causality? • How can you efficiently search and rank? • How do you build reliable models from complex data? Why are these questions important? To help and improve our society iCIS data science groups • Prof. Tom Heskes machine learning theory and applications • Prof. Peter Lucas Bayesian networks and eHealth • Dr. Elena Marchiori complex networks and machine learning • Prof. Theo van der Weide information systems and retrieval iCIS data science groups • Prof. Wessel Kraaij information retrieval and multimedia data analysis • Prof. Mireille Hildebrandt privacy and legacy aspects of data mining • Prof. Nico Karssemeijer computer-aided diagnosis and medical imaging • but also: Antal van den Bosch, Bert Kappen, Lutgarde Buydens, Marcel van Gerven, Maurits Kaptein, ... Course outline 1st semester 2nd semester 3rd semester 4th semester Track Basis Track Basis Track Choice Track Basis Research Seminar Track Choice Research Project CS & Society Master Thesis Track Choice Free Choice Track Choice Free Choice External Choice External choice Track basis courses • Mandatory, key methodological aspects • Machine Learning in Practice (6 ec) • Information Retrieval (6 ec) • Bayesian Networks (6 ec) Track choice courses • Statistical Machine Learning (6 ec) • Natural Computing (6 ec) • Machine Learning (9 ec) Theory and Tools • • • • • Computer aided diagnosis in medical imaging (6 ec) Bayesian Neurocognitive Modeling (6 ec) Bioinformatics (3 ec) Pattern Recognition for Natural Sciences (3 ec) Text Mining (6 ec) Applications • • • • Law in Cyberspace (6 ec) Foundations of Information Systems (6 ec) Cognition and Representation (6 ec) Business Rules Specification and Application (3 ec) Other aspects Research projects • Join one of the 7 research groups within iCIS • Can Google Trends predict outbreaks of influenza? Nature paper correlating Google searches to influenza outbreaks led to quite some discussion: a fluke or actual predictive power? • What distinguishes an excellent RTS game player from an average one? The SkillCraft data set contains many characteristics of various players that can be mined for actual causal relationships • Can we discover the structure of the brain and relate this to diseases such as Alzheimer? Time series data from neural recordings can be analyzed to distinguish healthy from non-healthy brains. Master thesis projects • Steffen Janssen developed a tool to predict productivity of software projects based on neural networks for the Dutch tax authorities • Thomas Janssen improved the fitting of hearing aids by machine learning for the hearing aid company GN ReSound • Louis Onrust studied a novel machine learning method for the extraction of brain structure from neuroimaging data Master thesis projects • Niels Radstake investigated Bayesian approaches to analyze mammographic images • Jelle Schühmacher came up with a classifier-based method for searching large document collections • Tom de Ruyter works on his master thesis at Xerox in Grenoble to improve dynamic pricing for parking in LA and other US cities Do you want to study abroad? Or an internship? For appointments please mail to: [email protected] Room HG 00.508 But first contact your study advisor about the contents of your stay abroad! Job perspective • Start up your own company in data analytics, become a data analysis specialist or consultant at a larger company, or go for a PhD Rasa Jurgelenaite Quantitative risk analyst at ABN AMRO Kristel Rösken Business analyst at VVV Nederland Pavol Jancura Software design engineer at ASML Laurens van de Wiel Data scientist at FlxOne Max Hinne and Wout Megchelenbrink PhD students Bart Bakker Senior scientist at Philips Research Alex Slatman Director at OBI4wan Why Data Science at the Radboud University? • Diversity: various aspects and applications of data science • Flexibility: large choice of courses to shape student interests • Excellence: students are embedded in research groups Example: Machine Learning in Practice • Basic idea: student teams enter an ongoing machine learning competition • While trying to beat the other teams, students learn the ins and outs of challenging machine learning problems • Example: learn to detect whale calls in order to prevent collisions • The Radboud team called UHURA ended in the top quarter of more than 200 contenders Example: Statistical Machine Learning • Theoretical underpinning of machine learning methods - regression - classification - neural networks - kernel methods - mixture models and EM • Programming and math exercises • Demonstrations on actual data Example: Natural Computing • Formerly bio-inspired algorithms • Basic idea: student teams choose a problem and solve it using bioinspired methods • My project: use mechanisms from immune systems to develop a method for optimization and implement this on a GPU Example: Bayesian Neurocognitive Modeling • Use machine learning tools to understand our brain • Example: decode fMRI data to reconstruct the image the person is looking at • Pioneered by Gallant's lab at UCB • In the course we implement similar techniques for still images. And that is just one week My impressions • • • • • • Is it fun? Is it difficult? Can you make a living? Will you have options? Can you reconsider? Study environment Should you do it? • Pro tips: - Have a look at some statistics before starting the courses - Always ask. Always.