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Data Science Master TrackTom Heskes and Niklas WeberScientific 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 groupsProf. Tom Heskesmachine learning theory and applications

Prof. Peter LucasBayesian networks and eHealth

Dr. Elena Marchioricomplex networks and machine learning

Prof. Theo van der Weideinformation systems and retrieval

iCIS data science groupsProf. Wessel Kraaijinformation retrieval and multimedia data analysis

Prof. Mireille Hildebrandtprivacy and legacy aspects of data mining

Prof. Nico Karssemeijercomputer-aided diagnosis and medical imaging

but also: Antal van den Bosch, Bert Kappen, Lutgarde Buydens, Marcel van Gerven, Maurits Kaptein, ...Course outline1st semesterTrack BasisTrack BasisTrack ChoiceTrack ChoiceFree Choice2nd semesterTrack BasisResearch SeminarTrack ChoiceTrack ChoiceFree Choice3rd semesterResearch ProjectCS & SocietyExternal ChoiceExternal choice4th semesterMaster ThesisTrack basis coursesMandatory, key methodological aspects

Machine Learning in Practice (6 ec)Information Retrieval (6 ec)Bayesian Networks (6 ec)Track choice coursesStatistical Machine Learning (6 ec)Natural Computing (6 ec)Theory and ToolsMachine Learning (9 ec)

Computer aided diagnosis in medical imaging(6 ec)Bayesian Neurocognitive Modeling(6 ec)Bioinformatics(3 ec)ApplicationsPattern Recognition for Natural Sciences(3 ec)Text Mining (6 ec)

Law in Cyberspace (6 ec)Foundations of Information Systems (6 ec)Other aspectsCognition and Representation (6 ec)Business Rules Specification and Application (3 ec)

Research projectsJoin 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 todiseases such as Alzheimer? Time series data from neuralrecordings can be analyzed to distinguish healthy fromnon-healthy brains.

Master thesis projectsSteffen 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 machinelearning for the hearing aid company GN ReSound

Louis Onrust studied a novel machine learning method for the extractionof brain structure from neuroimaging data

Master thesis projectsNiels Radstake investigated Bayesian approaches to analyze mammographic images

Jelle Schhmacher came up with a classifier-based method forsearching large document collections

Tom de Ruyter works on his master thesis at Xerox in Grenobleto 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:

io@science.ru.nl

Room HG 00.508

But first contact your study advisor about the contents of your stay abroad!

Job perspectiveStart up your own company in data analytics, become a data analysis specialist or consultant at a larger company, or go for a PhD

Rasa JurgelenaiteQuantitative risk analystat ABN AMROBart BakkerSenior scientistat Philips ResearchKristel RskenBusiness analystat VVV NederlandPavol JancuraSoftware design engineerat ASMLAlex SlatmanDirector at OBI4wanMax Hinne and Wout MegchelenbrinkPhD students

Laurens van de WielData scientist at FlxOneflxone: data driven advertising; obi4wan: social media monitoring

Why Data Science at the Radboud University?Diversity: various aspects and applications of data science

Flexibility: large choice of courses to shapestudent interests

Excellence: students are embedded inresearch groups

Example: Machine Learning in PracticeBasic 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 toprevent collisions

The Radboud team called UHURA ended in the topquarter of more than 200 contenders

Example: Statistical Machine LearningTheoretical underpinning of machine learning methodsregressionclassificationneural networkskernel methodsmixture models and EM

Programming and math exercises

Demonstrations on actual data

Example: Natural ComputingFormerly bio-inspired algorithms

Basic idea: student teams choose a problem and solve it using bio-inspired methods

My project: use mechanisms from immune systems to develop a method for optimization and implement this on a GPU

Example: Bayesian Neurocognitive ModelingUse machine learning tools to understand our brain

Example: decode fMRI data toreconstruct the image the person islooking at

Pioneered by Gallant's lab at UCB

In the course we implement similartechniques for still images. And thatis just one weekMy impressionsIs it fun?Is it difficult?Can you make a living?Will you have options? Can you reconsider?Study environmentShould you do it?

Pro tips:Have a look at some statistics before starting the coursesAlways ask. Always.NRW_upcall_quietnull2377.1433eng - iTunPGAP0eng - iTunNORM 00000013 00000013 00000023 00000023 00000497 00000497 0000119E 0000119E 00000497 00000497eng - iTunSMPB 00000000 00000210 00000AA7 0000000000018CC9 00000000 0000CB44 00000000 00000000 00000000 00000000 00000000 00000000NRW_upcall_noisynull2377.1433eng - iTunPGAP0eng - iTunNORM 00000019 00000019 00000058 00000058 000004B1 000004B1 0000228E 0000228E 00000497 00000497eng - iTunSMPB 00000000 00000210 00000AA7 0000000000018CC9 00000000 0000CB44 00000000 00000000 00000000 00000000 00000000 00000000