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Data S cience master track

Data S cience master track. S cientific questions you will study . What is clustering? What is causality? How can one efficiently search and rank? Can we build a reliable model from complex data ?. Why are these questions important? . To help and improve our society.

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Data S cience master track

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  1. Data Science master track

  2. Scientific questions you will study • What is clustering? • What is causality? • How can one efficiently search and rank? • Can we build a reliable model from complex data?

  3. Why are these questions important? To help and improve our society

  4. iCIS data science groups Prof. Heskes(master track coordinator) machine learning theory and applications Prof. Lucas Bayesian networks and eHealth Prof. Kraaij information retrieval and multi-media data analysis Prof. Van der Weide information systems and retrieval

  5. iCISdata science groups Dr. Marchiori complex networks and machine learning Prof. Hildebrandt privacy and legal aspects of data science Prof. Karssemeijer computer-aided diagnosis and medical imaging

  6. Mandatory and optional courses Mandatory Machine Learning in Practice(6 ec) Heskes Information Retrieval (6 ec) Van der Weide, Kraaij Bayesian and Decision Models in AI (6ec) Lucas, Velikova Optional Data Science Theory and Tools Statistical Machine Learning (6 ec) Wiegerinck, Claassen, Kappen, Heskes Bio-inspired Algorithms (3 ec*) Marchiori Evolutionary Algorithms (6 ec) Sprinkhuizen-Kuijper, Marchiori Machine Learning (6 ec) Kappen, Wiegerinck Data Science Applications Computer Aided Diagnosis in Medical Imaging (6 ec) Karssemeijer Bayesian Neurocognitive Modeling (6ec) Van Gerven Bioinformatics (3ec) Marchiori Pattern Recognition for Natural Sciences (3ec) Buydens and others Intelligent Information Tools (5 ec**) Van den Bosch Data Science Aspects Law in Cyberspace (6 ec) Hildebrandt Foundations of Information Systems (6 ec) Van Bommel Cognition and Representation (6 ec) Sarbo Business Rules Specification and Application (3 ec) Hoppenbrouwers

  7. 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 “Sushi” iscurrently in the top quarter of more than200 contenders spectogram with a typical whale call

  8. Example: Bio-inspired algorithms • Basic idea: student teams investigate diverse types of bio-inspired methods • The teams choose a problem and solve it using bio-inspired methods Example: use immune systems mechanisms to develop a method for image similarity search. Similarity Search using a Negative Selection Algorithm was accepted at ECAL 12 Advances in Artificial Life, 2013 target image top four retrieved images

  9. Research projects (afdelingsstages) Join one of the 7 research groups within the institute

  10. Can Google Trends predict outbreaks of influenza? [1] showed that outbreaks of influenza were correlated to numbers of Google searches for terms related to flu prevention and cure. This and other studies indicate correlations between search volume and (social) behavior, but do this "after the fact" and hence may be susceptible to overfitting. Do the suggested correlations also apply to novel data? Are other examples of predictive power of Google Trends? [1] http://www.nature.com/nature/journal/v457/n7232/full/nature07634.html

  11. Predicting protein three-dimensional structures Protein residue-residue contact prediction can be useful in predicting protein three-dimensional structures. PSICOV [1] is a state of the art method to predict contacts between residues of a target protein using information from the protein sequences of its family. Can we exploit biological knowledge to improve PSICOV? [1] http://bioinformatics.oxfordjournals.org/content/28/2/184.full

  12. Examples of master thesis projects Steffen Janssendeveloped a tool to predict productivity of software projects based on neural networks for the Dutch tax authorities Kristel Rösken applied data mining to social network profiles for LogicaBV Thomas Janssen improved the fitting of hearing aids by machine learning for the hearing aid company GN ReSound

  13. Examples of master thesis projects Louis Onrust studied a novel machine learning method for the extraction of brain structure from neuroimaging data Niels Radstake investigated Bayesian approaches to analyze mammographic images Jelle Schühmacher came up with a classifier-based method for searching large document collections

  14. Job perspectives A larger company as consultant or data analysis specialist or start up your own company in data analytics, or go for aPhD Quantitative risk analyst at ABN AMRO Bank (Rasa Jurgelenaite) Senior Scientist at Philips Research (Bart Bakker) Metrology Software Design Engineer at ASM (PavolJancura) Business Analist E-business at VVV Nederland BV (KristelRösken) OBI4wan (Alex Slatman) PhD students (Max Hinne, WoutMegchelenbrink)

  15. Unique aspects of our data science track Diversity: multiple aspects and applications of data science Flexibility: large choice of courses to shape student interests Excellence: students are embedded in research groups

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