Artificial Intelligence

Be a pioneer in Intelligent Systems

Master's programme in Artificial Intelligence

Artificial Intelligence (AI) is widely adopted in our society: we find applications of AI ranging from cars that detect pedestrians to Apple’s virtual assistant, Siri. Such applications use AI techniques to interpret information from a wide variety of sources and use it to enable intelligent, goal-directed behaviour.

The Vrije Universiteit Amsterdam’s AI Master programme offers two tracks that are oriented towards the practical application of AI technology in a broad, problem-oriented setting. Here, you can study AI technology from a societal perspective, looking into questions such as “how can we develop and evaluate computer-based technology that exploits knowledge about human functioning?”
There are two tracks: one focusing on how to interpret, support and adapt to human behaviour, another track focusing on understanding cognitive aspects of AI.

Track Socially Aware Computing

This track focusses on the application of AI in Socially Aware Computing, highlighting the analysis and application of new AI techniques to develop solutions that understand and can reason about their social context. Its goals are highly diverse, and range from optimizing internet searches to supporting elderly people in their struggle with dementia. You will learn how human behavior can be interpreted based on sensor data and computational models of physiological and cognitive processes. You will gain experience in integrating such models in dedicated applications that support humans in their daily lives, making these systems truly aware of human functioning.

In the specialization phase of the Socially Aware Computing track you study relevant courses with respect to a particular application area (e.g. support of people during exercising, or elderly care) or a relevant scientific discipline (e.g. psychology, sociology, movement sciences, biomedical sciences, criminology, etc.).

Track Cognitive Science

Immerse yourself in the multidisciplinary study of mind and cognition. Researchers in cognitive science come from a wide range of backgrounds, including psychology, computer science, artificial intelligence, philosophy, mathematics and neuroscience. They all share the common goal of gaining a deeper understanding of the human mind, for both theoretical and practical purposes.

The track focusses on the processes that underlie human functioning from two different research perspectives: empirical work and computational modeling. The combination of these two perspectives allows for a better understanding of the mechanisms underlying human functioning. For example, empirical work may suggest a functional layout for computation models, and vice versa, results of simulations with computation models can provide suggestions for setting up specific experiments. The underlying philosophy of Cognitive Science at the Vrije Universiteit Amsterdam is to challenge students to be knowledgeable in a wide variety of fields and techniques, all of which are related to the subject area of cognitive psychology.

The Cognitive Science track is jointly organized by the Department of Cognitive Psychology of the Faculty of Psychology and Education, and the Department of Artificial Intelligence of the Faculty of Sciences.

Joint AI programme

The Vrije Universiteit Amsterdam also participates in an AI master programme with the University of Amsterdam called “Artificial Intelligence in Amsterdam,” which takes a technical approach to AI, focussing on the understanding, analysis and development of novel AI algorithms. The collaboration in this master programme makes it possible to offer a range of topics of unparalleled breadth, all taught by active researchers who are experts in the respective areas. It builds on a basis of compulsory courses on core topics in AI and elective advanced courses in these or more specific AI topics.

For more details, see http://gss.uva.nl/future-msc-students/information-sciences/content26/artificial-intelligence.html

VU Amsterdam offers 2 Master's tracks in AI: Socially Aware Computing and Cognitive Science. Both tracks contain key courses in: 

Track Socially Aware Computing

This Master's track has a focus on the application of AI in Socially Aware Computing, highlighting the analysis and application of new AI techniques to develop solutions that understand and can reason about their social context. Key courses are: 

Track Cognitive Science


This Master's track focusses on the processes that underlie human functioning from two different research perspectives: empirical work and computational modeling. Key courses are: 

Joint AI-programme

This programme consists of the following elements:

Year 1

- Courses on core topics (42 ECTS):

  • Multi-Agent Systems
  • Computational Intelligence
  • Computer Vision
  • Information Retrieval
  • Knowledge Representation
  • Machine Learning
  • Natural Language Processing

Courses on the seven core topics are taught by leading researchers in their fields to provide you with a solid basis and understanding of current AI research.

- Advanced AI courses and Elective courses (18 EC)

Year 2

In the second year of your Master’s you specialise in one of the core topics, or you choose to continue with one of the combined profiles Data Science or Artificial Intelligence and the Web. The specialisation stage consists of:

- Advanced AI courses and Elective courses (24 ECTS)

- Master’s thesis (36 ECTS): for your thesis you join a VU or UvA research group or work with a company on an industrial problem.

Course descriptions

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.

Computational Intelligence
In Computational Intelligence (CI), we research techniques that achieve intelligence, or at least intelligent behaviour, by considering the behaviour that emerges from the interaction between relatively simple components in large collectives. The algorithms are often bio-inspired: they imitate aspects of behaviour found in nature. Examples are algorithms that mimic trail formation in ant colonies for optimal path planning. Evolutionary algorithms that mimic natural evolution to optimise robot control, or neural networks for predicting the course of a disease. CI employs these algorithms to develop systems that are adaptive, collective, autonomous and self-organising. In this profile, we particularly consider CI for systems like (future versions of) swarm robotic systems, smart environments, eHealth systems with interactive sensing devices and smart vehicles.

Because of the inherent complexity of CI systems and the difficulties in analytically predicting the behaviour that emerges, this profile has a strong experimental flavour: for CI algorithms, “the proof of the pudding is the eating”. As a student in Computational Intelligence you can gain real-life experience with applying and/or researching CI techniques. This can be through an internship at a company that exploits CI techniques, or you can become involved in one of our research projects in Evolutionary Robotics, Artificial Life and adaptive health systems. In the latter case, you will conduct proper scientific research aiming at a publication, typically at a prestigious conference.

Computer vision
Computer vision focuses on techniques and models for acquiring and analyzing images in order to understand objects and scenes in the real world. Computer vision is important for the construction of intelligent methods and techniques for (autonomous) systems that interpret sensory information and use that information to generate intelligent and goal-directed behavior.

Computer vision methods include image segmentation, object recognition and profiling, motion estimation, event detection, 3D scene reconstruction, human-behaviour analysis, faces and gesture recognition. The methods are studied using elements of geometry, physics and statistics.

Information Retrieval
The way we access, provide, and exchange information has changed dramatically with the rise of the Internet. Information retrieval studies and invents methods and techniques for the design, implementation, and use of information processing technology in the context of a variety of Internet applications, ranging from search engines to text analysis.

Information Retrieval has developed from a number of research areas, including Computer Science, Library Science, Artificial Intelligence, Data Mining, and Natural Language Processing. While Information Retrieval builds on techniques from a variety of research areas, there are a number of research problems that are specific to the Web applications, such as the design of Internet search engines, efficient linking of related information across the Web, improving information extraction from social networking sites, and the access of foreign language information. In addition, the sheer scale of the Internet opens up tremendous opportunities for data mining approaches, while at the same time posing interesting research challenges with respect to robustness and scalability.

Within the Information Retrieval profile you will be familiarized with several data mining, natural language processing, and link-based techniques that are not only relevant to this profile but also to many other Artificial Intelligence applications. It covers the well-established techniques within the area but is also looks forward, discussing the science behind cutting-edge technologies and anticipating Web technologies that yet have to be fully realized.

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.­

Machine Learning
In Machine Learning we develop algorithms that can improve their performance by learning from experience. Experience often comes in the form of very large amounts of data, or "Big Data". The resulting algorithms and models are used for making predictions and for improving decisions. It has become a core technology for a wide variety of applications such as: text and image classification; information retrieval, robot control; discovering causal explanations, social network analysis, customer intelligence; anomaly detection, recommender systems, fraud detection, forecasting and so on.

Due to the increased availability of data from sensors (Internet-of-Things), the range of applications is growing fast. The emphasis in this profile is on algorithms and statistical models that explain why and when algorithms work. We also discuss a number of algorithms in detail, such as clustering, dimensionality reduction, regression and classification, graphical models and deep learning. The profile has a strong mathematical component, but there is also an emphasis on developing the skills to implement machine learning algorithms through project assignments. As a student in Machine Learning you can do your master's thesis on a fundamental topic. e.g. developing a new general algorithm, but also on a more applied topic, e.g. developing an innovative application. Many students conduct their thesis research as an intern with a company.

Natural Language Processing
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 Science
This specialization focuses on understanding, analyzing and working with large amounts of data. Students study the entire Data Science lifecycle from data acquisition and management to analysis and visualization. These techniques include machine learning and data mining, large scale data management, information visualization and reasoning over web data.

There is a strong emphasis on applying artificial intelligence techniques to Data Science problems and in particular setting up experiments and performing informative analyses. Students will have the opportunity to apply their knowledge to large real world datasets like those from social media or the web. During the final Masters project, students will put together all facets of their education to tackle a data science problem.

AI and the Web
Since its invention in the early '90s, the Web has become the largest information space that has ever been constructed. The Web is not only a very large environment, but it is also very diverse, it combines text, images, video and data, it is very dynamic, noisy, and very, very large. That makes the Web a natural "habitat" for intelligent systems, and many typical AI problems can be investigated in this habitat: Can we build computers that can reason about the information in websites? Can we build search engines that really understand our question, and that give us intelligent answers? Can we build smart agents that travel across the web to collect personalised information? Can we use natural language processing to build computers that can read and understand web pages? Can we use machine learning techniques to automatically categorise webpages, or even to learn which webpages are trustworthy, and which ones are not? The interdisciplinary field of "AI on the Web" combines techniques from such diverse subfields as machine learning, natural language processing, knowledge representation and intelligent agents to tackle these challenging problems.


Artificial Intelligence courses in 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.

AI job chart

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

The job market

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

      AI job table

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.

CAMeRA 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.

   

Crowd control

Overview Artificial Intelligence

Language of instruction

English

Duration

2 years

Tuition fee

Application deadline

1 June for Dutch students. 1 April for EU/EEA and non-EU/EEA students.* * EU/EEA students with an international degree who do not need housing services through Vrije Universiteit Amsterdam University Amsterdam can still apply until 1 June.

Start date

1 September

Study type

Full-time

Specializations

Cognitive Science, Human Ambience, Intelligent Systems Design, Webscience

Admission requirements

Field of Interest

Behavioural and Social Sciences/Computer Science, Mathematics and Business

Crowd simulation

  • 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

Arjon Buikstra
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 University 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 University 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.

E mate

Dutch Students

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

Specific requirements master’s programme

  • Students with a Dutch preliminary education
    For the Master programmes in Artificial Intelligence, students may enrol who have a Bachelor or Drs diploma in Artificial Intelligence obtained at a Dutch university (Utrecht, Nijmegen, Amsterdam (VU, UvA), Groningen, Maastricht). Some of the programmes are open to students who have a Bachelor, Drs or Master diploma in Computer Science, Psychology, Biology, or Law obtained at a Dutch institute or university of quality recognized by VU University Amsterdam. Some of the programmes are also open to people with other diplomas, University or HBO, who are kindly invited to contact us if interested in following a Master programme at our Department.
  • 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 with appropriate specialization. The student is assumed to be familiar with programming in Java and Prolog, to have a general knowledge of Artificial Intelligence and a basic working knowledge of computer science, logic, mathematics, psychology and natural language processing. Under specific circumstances other students may also be admissible.

General language proficiency requirements
VU University Amsterdam requires international 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 or who have obtained an international Baccalaureate or European Baccalaureate diploma.

Admission to a Master’s programme: the Bachelor-before-Master rule
The Bachelor-before-Master rule (‘harde knip’) will be applied to all VU programmes as of 1 September 2013. This means that you can only start a Master’s programme on 1 September 2013 if you have obtained your Bachelor’s degree. Uncompleted Bachelor’s subjects are not permitted if you want to start a Master programme.

What does this mean now for students?
You may have to adjust your study plan. For example, if you’re planning a semester abroad in the first semester of the academic year 2012-2013, and you would like to start the following Master’s programme in September 2013, please note that every part of the Bachelor’s study programme has to be completed – not only the compulsory parts of the programme. Other (short) interruptions of your study programme can also have an effect on your ability to proceed onto a Master’s programme. If the Bachelor’s programme is not fully completed, you cannot start the Master’s programme until the start of the next academic year. Take this into account when planning your study path! Please contact our faculty’s study advisors when you have any questions.

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 study advisor:

Mark Hoogendoorn

VU University Amsterdam
Faculty of Sciences
Dr. Mark Hoogendoorn
De Boelelaan 1083a, T-333
1081 HV Amsterdam 
T  +31 (0)20 598 7772
E m.hoogendoorn@vu.nl 


Would you like to read the key points of the Master's programme? Order the brochure. Or find out more about the Master's programmes and visit our information days.


Master's evening: 6 December

International Students

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

Specific requirements master’s programme

  • Students with a Dutch preliminary education
    For the Master programmes in Artificial Intelligence, students may enrol who have a Bachelor or Drs diploma in Artificial Intelligence obtained at a Dutch university (Utrecht, Nijmegen, Amsterdam (VU, UvA), Groningen, Maastricht). Some of the programmes are open to students who have a Bachelor, Drs or Master diploma in Computer Science, Psychology, Biology, or Law obtained at a Dutch institute or university of quality recognized by VU University Amsterdam. Some of the programmes are also open to people with other diplomas, University or HBO, who are kindly invited to contact us if interested in following a Master programme at our Department.
  • 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 with appropriate specialization. The student is assumed to be familiar with programming in Java and Prolog, to have a general knowledge of Artificial Intelligence and a basic working knowledge of computer science, logic, mathematics, psychology and natural language processing. Under specific circumstances other students may also be admissible.

General language proficiency requirements
VU University Amsterdam requires international 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 or who have obtained an international Baccalaureate or European Baccalaureate diploma.

Admission to a Master’s programme: the Bachelor-before-Master rule
The Bachelor-before-Master rule (‘harde knip’) will be applied to all VU programmes as of 1 September 2013. This means that you can only start a Master’s programme on 1 September 2013 if you have obtained your Bachelor’s degree. Uncompleted Bachelor’s subjects are not permitted if you want to start a Master programme.

What does this mean now for students?
You may have to adjust your study plan. For example, if you’re planning a semester abroad in the first semester of the academic year 2012-2013, and you would like to start the following Master’s programme in September 2013, please note that every part of the Bachelor’s study programme has to be completed – not only the compulsory parts of the programme. Other (short) interruptions of your study programme can also have an effect on your ability to proceed onto a Master’s programme. If the Bachelor’s programme is not fully completed, you cannot start the Master’s programme until the start of the next academic year. Take this into account when planning your study path! Please contact our faculty’s study advisors when you have any questions.

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 University Amsterdam, the Dutch governement and other organisations offer scholarships, fellowships and grants.

For further information about admission to the programme you can contact the study advisor:

Stefan Schlobach

VU University Amsterdam
Faculty of Sciences
Dr. Stefan Schlobach
De Boelelaan 1083a, T-365
1081 HV Amsterdam
The Netherlands 
T +31 (0)20 598 7678
E k.s.schlobach@vu.nl



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

Email: admissionsfs@vu.nl

General information about VU University Amsterdam

Please phone us at +31 (0)20 598 5000 (Monday – Friday, 10:00 to 12:00). You may also e-mail us at study@vu.nl.

Would you like to read the key points of the Master's programme? Order the brochure. Or find out more about the Master's programmes and visit our information days.


Master's evening: 6 December

Embodied Evolution