• Evolutionary computing
• Cognitive psychology and its applications
• Multi agent systems
• Social Robotics
• Knowledge Representation
• Natural Language Process Technology
• Data Mining
• AI & Society
  
  

• Seminar Cognitive Neuroscience
• Brain Imaging
• Neural models of cognitive processes
• Memory and Memory Disorders
  
  

• Evolutionary computing
• Cognitive psychology and its applications.
  
  

Evolutionary Computing
The course is treating various algorithms based on the Darwinian evolution theory. Driven by natural selection (survival of the fittest), an evolution process is being emulated and solutions for a given problem are being "bred". During this course all "dialects" within evolutionary computing are treated (genetic algorithms, evolution strategies, evolutionary programming, genetic programming). Applications in optimization, constraint handling, machine learning, and robotics are discussed.

Cognitive psychology and its applications
The course covers a number of central principles from the area of cognitive psychology and how these principles can be applied in the design of modern man-machine systems including human-computer systems. A variety of topics will be discussed such as mental workload, decision-making, driving behavior, route finding, medical decision-making and display design. The students will understand the role of human cognitive capabilities and limitations in the design of products, work places, and large systems. They will get acquainted with main areas of human factors and with main theories and findings on human performance. They will understand where in the process to apply knowledge and what methods can be used to analyze human performance. They will also learn how to approach and solve an applied problem in man-machine systems.

Multi agent systems
A key goal of artificial intelligence is to develop agent systems that can make decisions and complete tasks without direct human supervision. Agent systems focuses on completing the perception-action loop: given the results of such perception, how should an agent act in order to reach its goal, maximise its utility, and minimise its costs? Autonomous robots are a prototypical example of such systems, though an agent system can also be computer that plays board games like chess and go, or a search engine that meets information needs and offers recommendations. An interesting setting is that of multiple agents that collaborate and communicate to behave intelligently. This involves understanding each others goals and perceptions, and planning actions collaboratively.

Knowledge Representation
When humans reason about the world, we identify objects, we make categories of such objects, and we reason about the relations between the things in the world around us. How can we represent such knowledge in a computer, in such a way that a computer could reason about the world around it in a similar way? The field of Knowledge Representation and Reasoning aims to represent knowledge in such a form that a computer system can use it to solve complex tasks such as diagnosing a medical condition or having an intelligent dialog in a natural language. Knowledge representation and reasoning uses logic as its main mathematical tool, and tries to answer such questions as: how can we design logics that can efficiently reason with very large amounts of knowledge? Which logics are suited for reasoning about space and time? How can we deal with uncertainty and vagueness? How to reason about changes in the world around us? Knowledge Representation techniques are used in many practical applications. Examples are expert systems for medical diagnosis, decision support systems for judges, and intelligent dialogue systems such as Siri on the iPhone.

Natural Language Proces Technology
Over the past few years, research towards natural language processing has shown strong evidence as to the effectiveness of models that involve both hierarchical structure as well as statistical learning from corpora. In this profile you will study the state-of-the-art statistical models for complex language processing tasks such as parsing, language modeling and machine translation.
A characteristic of some of these models is that they involve defining probability measures over hierarchical structure, e.g., trees and graphs. The profile covers supervised as well as unsupervised methods for learning these models directly from large training corpora and provides the necessary background for research in Computational Linguistics and Natural Language Processing.

Data Mining
The course will provide a survey of basic data mining techniques and their application for solving real life problems. After a general introduction to Data Mining we will discuss some “classical” algorithms like Naive Bayes, Decision Trees, Association Rules, etc., and some recently discovered methods such as boosting, Support Vector MAchines, and co-learning. A number of successful applications of data mining will also be discussed: marketing, fraud detection, text and Web mining, possibly bioinformatics.

Read more about the study programme in the Study Guide.

A Master of Science degree in Artificial Intelligence gives you a strong foundation for working in key positions, in knowledge-intensive research centres or business.

Job Prospects

Examples of positions our alumni currently hold are: 

· Software Engineer at Google

· Data Scientist at Airbnb / Booking.com

· Project Manager at Volvo Car Group

· Computer Vision Expert at Eagle Vision

· PhD student at Royal Institute of Technology, Stockholm

Overall, the career perspectives for AI graduates are good. Most of our alumni find a job within three months after graduation.

Academic staff

All the Master’s courses are taught by researchers who are experts in their domain. This ensures you an advanced academic level of education, and integration of the latest developments in the field. The majority of lecturers are also involved in collaborative projects with industry players, creating a link to applications in real-world situations. And the active role our lecturers at international conferences contributes to solid and state-of-the-art course material.

Students

The Master's programme in Artificial Intelligence will have a student population of approximately 75 students each year, with many nationalities and backgrounds. Courses take place in small groups which leads to an informal teaching environment. As a graduate student you are encouraged to regularly present and discuss your work, to optimally learn from the staff and your fellow students.

Pioneer in developing intelligent systems

This programme is a pioneer in the development of intelligent systems. As a Master's student, you will be given the opportunity to work on advanced information systems at a wide range of companies and institutions. Some recent examples include:

• Semantic navigation on overheid.nl (the main Dutch government website)
• A personal 'quit assistant' to help people give up smoking 
• In cooperation with Philips, adaptive personal music choices during sports training
• New forms of online publication for Elsevier
• A knowledge system to predict problems with Amsterdam's trams and other public transport
• An intelligent opponent that is able to antipate on player's actions in a real time action game

Research institutes

The joint Master's programme in Artificial Intelligence is strongly connected to research topics of the informatics research institutes of both universities.
The Network Institute brings together researchers from many different academic disciplines, including information systems, communication science, computer science, business and management research, knowledge management, marketing and strategy, economics, artificial intelligence, mathematics, and organization science. The Network Institute is part of VU Amsterdam.

The Network Institute provides an environment for the study and the development of media applications, with the focus on their impact on people's physical and mental wellbeing. CAMeRA recognizes its mission in both fundamental research and applied projects to be socially responsible in nature.

The mission of the Informatics Institute is to perform curiosity driven and use-inspired fundamental research in Computer Science. The research in the institute involves complex information systems at large, with a focus on Collaborative, Data Driven, Computational and Intelligent Systems, all with a strong interactive component.

What are the mathematical properties of information? How can we describe how information flows between humans or computers? Questions such as these lie at the heart of the research conducted at the Institute for Logic, Language and Computation (ILLC), a world-class research institute in the interdisciplinary area between mathematics, linguistics, computer science, philosophy and artificial intelligence.

• Structuring information
  How can you bring structure to the information on the internet? Is it possible to make the internet smarter and more personal?  
• What turns information into knowledge?
  Introducing structure, so that the computer can do something with it. But how do you generate the right knowledge? And how do you make sure the computer knows what to do with the information presented? 
• Learning from data
  There are millions of hard disks full of information reflecting our world. What can you learn about the real world from that mass of digital information?   
• Supporting humans in an intelligent manner with awareness of the human’s state
  How can you make intelligent support systems (or agents) that are aware of the current functioning of the human and can give dedicated support based upon this state of the human.

Each Master’s programme concludes with a graduation project or internship. This can be an individual project or a group project. Internships proposed by the student need approval in advance from a member of staff, who will also be involved with supervising the project. For details, please visit the site of the Computer Science department.

Rianne van Lambalgen, graduate Artificial Intelligence - specialization Cognitive Science

"After finishing my studies in Cognitive Psychology I started the specialization Cognitive Science, part of the Master’s programme in Artificial Intelligence. I enjoyed this programme very much as it was a good combination of technically and theoretically challenging material. I learned about theories in psychology, but also their application within artificial intelligence. 

For me, this course emphasized the practical use of scientific research, which is one of the reasons I started my current PhD position at the Agent Research group at the department of Artificial Intelligence. 

In addition to the interesting content, joining this Master’s programme was also fun as the group is relatively small and practical work is often done in small groups, which gives you a good opportunity to meet people."

Arjon Buikstra conducted his master's thesis in Berlin

Experience abroad:

• three months in California as part of his Bachelor’s degree
• a Master’s project in Berlin

Fun in a whole new environment

Through ISEP, I had the chance to study in California for a few months during the third year of my studies. It was great fun to be in a whole new environment, studying completely different courses to the ones I would have taken in the Netherlands and getting to know all kinds of different people. Since my time in America had been so much fun, when it came to my Master’s project I once again started looking for the chance to venture a little further afield.

Two teachers and a little luck

More or less by accident I came across a project in Berlin. I was sitting an exam in a lecturer’s office when another lecturer came in. She later sent me an e-mail, apologizing for interrupting me. At the same time she asked if I might be interested in a project in Berlin. I sure was! So in the end it was down to luck and help from two teachers that I came into contact with the researchers at the Max Planck Institute for Human Development in Berlin.

Three months in Berlin

Eventually I spent three months living in Berlin. Every day I went to the Max Planck Institute, flipped open my laptop and began working with the LarKC workflow (in Eclipse). LarKC stands for Large Knowledge Collider. It’s a platform with a smart way of finding the right information among masses of data, like the billions of facts on Wikipedia, combined with iMDB. The aim of my Master's project was to improve the search features in these types of large information files. My challenge was finding search and filter algorithms - strategies for providing maximum results with minimal input. I was in touch with my supervisors at VU Amsterdam on a weekly basis via Skype. There was always a new path to unravel or a difficult question to answer.

Fascinating summer school

The research group I was working with holds an annual summer school, which I was allowed to attend during my project. There I learned a great deal about Decision Theory, and Bounded Rationality in particular. I’d already had a taste of this during my studies at VU Amsterdam, but it was great to learn everything there is to know at this time from the true experts in the field. 
Decision Theory is all about determining the consequences of decisions. Within this field, Bounded Rationality takes into account the limited availability of information, cognitive constraints and the limited time available to reach a decision. The weighted importance of limited information in decision-making and the uncertainty factor make it a theory you can tinker with endlessly.

Lifelong memories

I would recommend studying abroad to anyone. I wouldn’t want to have missed a single one of the experiences I’ve had. You are more or less forced to meet a lot of new people and to discover new places. It's not the evenings at home or the weekends relaxing that you remember later, but that one weird night out that ended with four of you trying to push over the Berlin Wall at sunrise. Above all, the weekends in Berlin were much more fun than if I’d stayed at home! It deserves its reputation as an open-minded party town for sure - and the low rent is nothing to complain about either.

The requirements are split up into Masters’ specialization specific requirements and general requirements.

Specific requirements master’s programme
Students with a Dutch preliminary education:
Admission to this Master programme is open to students with a Bachelor degree in Artificial Intelligence or students from Computer Science or Psychology. The student is assumed to have programming skills (Python or Java), to have a general knowledge of Artificial Intelligence and a basic working knowledge of computer science, logic, mathematics, and psychology.
Under specific circumstances other students may also be admissible. Students with other diplomas, University or HBO, who are kindly invited to contact us if interested in following a Pre-Master programme at our Department. Applicants without an Artificial intelligence or Computer Science Bachelor Degree are required to take a Programming test in Python. 

General language proficiency requirements
VU Amsterdam requires English at VWO level. Therefore, international applicants are required to take an English test and to submit their score as a part of the application. Exceptions are made for students who have completed their education in Canada, USA, UK, Ireland, New Zealand or Australia or who have obtained an international Baccalaureate or European Baccalaureate diploma.

Check all general information on admission and application to Master's programmes.

Always contact the Master's coordinator for advice before sending in your application.

For further information about admission to the programme you can contact the Master's coordinator:

VU Amsterdam
Faculty of Sciences
Dr. Jakub Tomczak
NU-gebouw 10A-85
De Boelelaan 1111,
1081 HV Amsterdam 
T +31 (0) 20 59 87772
E j.m.tomczak@vu.nl 

Or contact the programme director dr. Annette ten Teije: annette.ten.teije@vu.nl

Find out more about the Master's programmes and visit our information days.

The requirements are split up into Masters’ specialization specific requirements and general requirements.

Specific requirements master’s programme
Students with an international preliminary education: 
Admission to this Master programme is open to students with a Bachelor degree in Artificial Intelligence or students from Computer Science or Psychology. The student is assumed to have programming skills (Python or Java), to have a general knowledge of Artificial Intelligence and a basic working knowledge of computer science, logic, mathematics, and psychology. Under specific circumstances other students may also be admissible. Applicants without an Artificial intelligence or Computer Science Bachelor Degree are required to take a Programming test in Python.

General language proficiency requirements
VU Amsterdam requires all applicants to take an English test and to submit their score as a part of the application. Exceptions are made for students who have completed their education in Canada, USA, UK, Ireland, New Zealand or Australia.

Check all general information on admission and application to Master's programmes here.

Always contact the Master's coordinator for advice before sending in your application.

There are several possibilities for obtaining funding. VU Amsterdam, the Dutch governement and other organisations offer scholarships, fellowships and grants.

For further information about admission to the programme you can contact the programme director:
dr. Annette ten Teije: annette.ten.teije@vu.nl

Would you like to know more about our courses, scholarships and application & registration procedure? Please contact our International Office.

Email: masters.fs@vu.nl

General information about VU Amsterdam

Please phone us at +31 (0)20 598 5000 (Monday – Friday, 9.00 to 12:00). You may also email us at study@vu.nl.

Find out more about the Master's programmes and visit our information days.