1. Data Science Master Track
Tom Heskes and Niklas Weber

2. 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?

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

4. 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

5. 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, ...

6. Course outline 1st semester Track Basis Track Choice Free Choice
2nd semester
Research Seminar
3rd semester
Research Project
CS & Society
External Choice
External choice
4th semester
Master Thesis

7. Track basis courses Mandatory, key methodological aspects
Machine Learning in Practice (6 ec)
Information Retrieval (6 ec)
Bayesian Networks (6 ec)

8. Track choice courses Statistical Machine Learning (6 ec)
Natural Computing (6 ec) Theory and Tools
Machine Learning (9 ec)
Computer aided diagnosis in medical imaging (6 ec)
Bayesian Neurocognitive Modeling (6 ec)
Bioinformatics (3 ec) Applications
Pattern Recognition for Natural Sciences (3 ec)
Text Mining (6 ec)
Law in Cyberspace (6 ec)
Foundations of Information Systems (6 ec) Other aspects
Cognition and Representation (6 ec)
Business Rules Specification and Application (3 ec)

9. 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.

10. 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

11. 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

12. Do you want to study abroad? Or an internship?
For appointments
please mail to:
Room HG
But first contact your study advisor about the contents of your stay abroad!

13. 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
flxone: data driven advertising; obi4wan: social media monitoring
Bart Bakker
Senior scientist at Philips Research
Laurens van de Wiel Data scientist at FlxOne
Rasa Jurgelenaite Quantitative risk analyst at ABN AMRO
Pavol Jancura Software design engineer at ASML
Kristel Rösken
Business analyst at VVV Nederland
Alex Slatman Director at OBI4wan
Max Hinne and Wout Megchelenbrink PhD students

14. 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

15. 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

16. 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

17. Example: Natural Computing
Formerly 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

18. 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

19. 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.