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Data Science master track. Scientific questions you will study What is clustering? What is causality? How can one efficiently search and rank? Can we.

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Presentation on theme: "Data Science master track. Scientific questions you will study What is clustering? What is causality? How can one efficiently search and rank? Can we."— Presentation transcript:

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 Elena Marchiori Twan van Laarhoven Antal van den Bosch Janos Sarbo Marcel van Gerven Max Hinne Elena Sokolova Perry Groot Tom Heskes Tom Claassen Complex systems & machine learning Information Retrieval Bio-medical data analysis Knowledge representation Brain-image analysis Learning causal models Probabilistic Machine Learning Nico Karssemeijer Machine Learning Bert Kappen Lutgarde Buydens Maya Sappelli Wessel Kraaij Saskia Kodijk Theo van der Weide Mireille Hildebrandt Diagnostic Image Analysis Text mining Chemo Informatics Peter Lucas Cyber Law Suzan Verberne Researchers working on these questions Wout Megchelenbrink

5 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

6 iCIS data 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

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

8 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” is currently in the top quarter of more than 200 contenders spectogram with a typical whale call

9 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

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

11 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]

12 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]

13 Examples of master thesis projects Steffen JanssenSteffen Janssen developed a tool to predict productivity of software projects based on neural networks for the Dutch tax authorities Kristel RöskenKristel Rösken applied data mining to social network profiles for Logica BV Thomas JanssenThomas Janssen improved the fitting of hearing aids by machine learning for the hearing aid company GN ReSound

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

15 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 (Pavol Jancura) Business Analist E-business at VVV Nederland BV (Kristel Rösken) OBI4wan (Alex Slatman) PhD students (Max Hinne, Wout Megchelenbrink)

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


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