Data Science master track

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?

Why are these questions important?

To help and improve our society

iCIS data science groups

Prof. Heskes (master track coordinator)machine learning theory and applications

Prof. Lucas Bayesian networks and eHealth

Prof. Kraaijinformation retrieval and multi-media data analysis

Prof. Van der Weide information systems and retrieval

iCIS data science groups

Dr. Marchiori complex networks and machine learning

Prof. Hildebrandtprivacy and legal aspects of data science

Prof. Karssemeijercomputer-aided diagnosis and medical imaging

Mandatory and optional coursesMandatoryMachine Learning in Practice (6 ec) HeskesInformation Retrieval (6 ec) Van der Weide, KraaijBayesian and Decision Models in AI (6ec) Lucas, Velikova OptionalData Science Theory and ToolsStatistical Machine Learning (6 ec) Wiegerinck, Claassen, Kappen, HeskesBio-inspired Algorithms (3 ec*) MarchioriEvolutionary Algorithms (6 ec) Sprinkhuizen-Kuijper, MarchioriMachine Learning (6 ec) Kappen, Wiegerinck Data Science ApplicationsComputer Aided Diagnosis in Medical Imaging (6 ec) KarssemeijerBayesian Neurocognitive Modeling (6ec) Van GervenBioinformatics (3ec) MarchioriPattern Recognition for Natural Sciences (3ec) Buydens and othersIntelligent Information Tools (5 ec**) Van den Bosch Data Science AspectsLaw in Cyberspace (6 ec) HildebrandtFoundations of Information Systems (6 ec) Van BommelCognition and Representation (6 ec) SarboBusiness Rules Specification and Application (3 ec) Hoppenbrouwers 

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

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 12Advances in Artificial Life, 2013

target image

top four retrieved images

Research projects (afdelingsstages)

Join one of the 7 research groups within the institute

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

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 toimprove PSICOV?

[1] http://bioinformatics.oxfordjournals.org/content/28/2/184.full

Examples of master thesis projects

Steffen Janssen developed 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 Logica BV

Thomas Janssen improved the fitting of hearing aids by machine learning for the hearing aid company GN ReSound

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

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)

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