Artificial Intelligence: Cognitive Science

AI for Human Society

Multidisciplinary study of mind and cognition

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 VU 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.

Download the brochure Artificial Intelligence: Cognitive Science
VU Amsterdam offers a AI master and a special track Cognitive Science.

Master Artificial Intelligence
The focus of the Master Artificial Intelligence is Hybrid Intelligence.
Key courses of this Master are:
  • Evolutionary computing
  • Cognitive psychology and its applications
  • Multi agent systems
  • Social Robotics
  • Knowledge Representation
  • Natural Language Process Technology
  • Data Mining
  • AI & Society
Track Cognitive Science
The Cognitive Science track has an additional component of three courses to qualify for the AI Cognitive Science Diploma. A variety of courses can be used for this component:
  • Seminar Cognitive Neuroscience
  • Brain Imaging
  • Neural models of cognitive processes
  • Memory and Memory Disorders

Course descriptions of the AI-core courses
  • 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.

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.  

Arjon Buikstra

Student

"After studying a few months in California, I decided to do my master's project abroad as well. Eventually I spent three months living in Berlin, working at the Max Planck Institute for Human Development."

Overview Artificial Intelligence: Cognitive Science

LANGUAGE OF INSTRUCTION

English

DURATION

2 years

TUITION FEE

APPLICATION DEADLINE

1 May for non Dutch EU/EEA and non-EU/EEA students*
15 July for holders of a Dutch bachelor’s degree (with a Dutch or EU/EEA nationality).
* non Dutch EU/EEA students with an international degree who do not need housing services through VU Amsterdam can still apply until 15 July.

START DATE

1 September

STUDY TYPE

Full-time

FIELD OF INTEREST

Behavioural and Social Sciences
Health and Movement
Computer Science, Mathematics and Business
Language and Communication

Rianne van Lambalgen

Graduate

"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."

Dutch students

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 this Programming test in Python.
  


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 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 

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

International students

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 this Programming test in Python.

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 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 


Or 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: 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 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.

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