M.Sc. Artificial Intelligence

University of Amsterdam

Application deadline: Start in 1 February: December (non-EEA: October). Start in 1 September: May (non-EEA: February).
Tuition fee:
  • € 1,906 / Year (EEA)
  • € 12,000 / Year (Non-EEA)
Start date: September  2014, February  2015, September  2015
Credits (ECTS): 120 ECTS
Duration full-time: 24 months
  • English
Delivery mode: On Campus
Educational variant: Full-time

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This programme has a strong technical component. The focus is on the development and understanding of intelligent computational processes to create useful artefacts and to better understand (human) in

The AI programme at the University of Amsterdam has a strong technical component. The primary focus is on the development and understanding of intelligent computational processes for the benefit of both creating useful artefacts and helping better understand (human) intelligence. In the programme you acquire a working knowledge of efficient, robust and intelligent methods for interpreting (sensory) information of different modalities. The programme consists of a broad introductory part in the first semester. After this you get courses with a focus in one or two out of the following topics: * Gaming * Computer Vision * Machine Learning * Natural Language Processing * Information Retrieval


Programme internationalization:

This programme has a workload of 120 ECTS.

Computer Vision:

Intelligent methods and techniques are of vital importance of any intelligent autonomous system, which perceives and acts. This includes the formalization, generalization and learning of goal-directed behaviour in autonomous systems such as autonomous vehicles, robots, or visual serving systems. Further, this programme focuses on methodologies to create intelligent multimedia-information systems to access and classify multimodal information such as the retrieval/search of documents/images/videos, data mining, and intelligent agents on the Internet. Main topics are machine learning, computer vision, robots and autonomous systems, multi-agent systems.

Machine Learning:

Machine Learning uses experiences to construct a general model and improve its performance. Learning methods are used in a variety of systems including:systems for: * data mining; * text and image classification; * recognition of objects and information in texts; * robot control. Emphasis in this track is on algorithms, models for learning, theories that explain why algorithms work (Bayesian statistics, Reinforcement Learning and Minimal Description Length), multi-agent reinforcement learning and transfer learning for multiple modalities.

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. This track studies 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 track 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.

Information Retrieval:

The Internet has become an integral part of our society and economy over the last two decades. The way we access, provide, and exchange information has changed dramatically with the rise of the Internet. Within the Information Retrieval track you will be familiarized with several data mining, natural language processing, and link-based techniques that are not only relevant to this track but also to many other Artificial Intelligence applications.


Past and current computer games are covered with a special focus on game-AI. This track provides knowledge on: * game programming; * serious gaming and simulations; * learning in games; * multimedia analysis. Both human-computer interaction and visualisation techniques are subject, from the perspective of image processing, computer vision, virtual reality, and multimedia systems. The emphasis of this programme is on the application of AI for gaming, such as learning and intelligent techniques, (e.g. machine learning and pattern recognition), automated learning, and multimedia understanding.


Grade average

Previous education

Additional language requirements: * Cambridge Certificate in Advanced English: B1
* Cambridge Certificate of Proficiency in English: B1
* IELTS overall band: 6.5
* IELTS listening: 6
* IELTS reading: 6
* IELTS writing: 6
* IELTS speaking: 6
* TOEFL paper based: 580
* TOEFL computer based: 235
* TOEFL internet based: 90

English Language Requirements

IELTS band: 6.5
CAE score: (read more)

Cambridge English: Advanced (CAE) is part of the Cambridge English suite and is targeted at a high level (IETLS 6.5-8.0). It is an international English language exam set at the right level for academic and professional success. Developed by Cambridge English Language Assessment - part of the University of Cambridge - it helps you stand out from the crowd as a high achiever.

75 (Grade B)
TOEFL paper-based test score : 580
TOEFL iBT® test: 90


Scholarships / Grants:
The University of Amsterdam:The University of Amsterdam provides a limited number of full and partial scholarships for excellent students from outside the European Economic Area. The Amsterdam Merit Scholarships have specifically been designed to offer talented, ambitious and dedicated students the opportunity to pursue a MastFor additional information: Website

:Students from Columbia can apply for this scholarship through their website. For additional information: Website



Accredited by: NVAO in: Netherlands

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