Artificial Intelligence (AI) is a field that develops intelligent algorithms and machines. Examples include: self-driving cars, smart cameras, surveillance systems, robotic manufacturing, machine translations, internet searches, and product recommendations. Modern AI often involves self-learning systems that are trained on massive amounts of data ("Big Data"), and/or interacting intelligent agents that perform distributed reasoning and computation. AI connects sensors with algorithms and human-computer interfaces, and extends itself into large networks of devices. AI has found numerous applications in industry, government and society, and is one of the driving forces of today's economy.
Examples of positions our alumni currently hold are:
The two year programme consists of obligatory core courses, advanced AI courses and elective courses, a specialisation and thesis. In your first year you take the obligatory core courses. In the second semester of the first year you choose your specialisation in an area of your choice. In the second year you work on your specialisation and on your thesis.
The curriculum of the Master’s Artificial Intelligence leaves ample space for you to explore what you find interesting in and around the field of AI. You can specialise in one of the core topics or you can follow a direction that cuts across research areas.
Courses on core topics (42 EC)
These topics are taught by leading researchers in their fields and provide you with a solid basis and understanding of current AI research.
Advanced AI courses and Elective courses (18 EC)
In the second year of the Master’s programme you choose a specialisation in an area of your choice.This can be in a core topic or in your own designed specialisation. Both specialisations work towards a Master’s thesis through research with an associated research group at UvA or VU or in a company with an industrial problem.
The specialisation stage consists of:
A few examples of specialisations that you can do:
At the core of Data Science are methods for analysis of large volumes of data. Recently much more data has become available in electronic form, methods for analysis and modelling these data for prediction, classification and optimisation have become much more effective. Recent technical innovations, such as Deep Learning, provide increasingly powerful tools that make it possible to find complex patterns in very large datasets.
Much of the Master's AI is about Data Science. The obligatory courses on Machine Learning address key technology and theory for modelling large amounts of data. The courses on Machine Learning, Natural Language Processing, Information Retrieval and Computational Intelligence all have a strong focus on data-driven methods. For the “AI courses” in the curriculum students can choose advanced courses on these topics: Machine Learning 2, Computer Vision 2, Natural Language Processing 2, Information Retrieval 2, Deep Learning, Data Mining Techniques, Information Visualisation and Probabilistic Robotics. All these courses are about modelling data. These can be complemented by courses outside AI, for example on distributed computer systems, privacy and ethical questions, or on statistics.
Many applications of AI involve the use of the World-wide Web. The WWW makes information from around the world available. The ability to find information in this enormous resource and to combine different pieces of information to answer complex questions is a challenge for AI. Data available from the web make it possible to find the context of a query and thereby produce a better ranking. The solution will be in combining information search and retrieval with reasoning methods.
The Master's programme AI makes it easy to take this as a focus. AI courses for this specialisation are Information Retrieval 2, Knowledge Representation on the Web and Knowledge Engineering. These courses extend and deepen the knowledge from the first year courses Information Retrieval 1 and Knowledge Representation. The courses Natural Language Processing 2 and Applied Language Technology further extend this knowledge to natural language interactions and to machine translation, enabling queries to be answered from information in multiple languages.
Students with a Dutch Bachelor's degree in Artificial Intelligence or Computer Science may be admitted to the programme.
Students with a Bèta-Gamma or Future Planet Studies bachelor degree from the UvA with a major in AI may also be admitted.
Other students, who hold at least a Bachelor's degree in an area closely related to AI, may also apply. The main requirements are:
Applicants may be required to take additional courses before admission or during the Master's programme.
After registering in Studielink, within 48 hours you will receive an email with your UvA student number (UvA-net ID) and an email with instructions for the next step of the application process. You need your UvA-net ID to submit your online application via Datanose.
In Datanose, we expect you to upload the following documents:
Applicants are expected to have an overall grade point average (GPA) equivalent to at least:
All our international programmes are conducted in English, therefore, applicants must show their ability to write and speak in English on an academic level. Students with a Bachelor’s degree from a Dutch university and students who successfully finished a full academic programme at an esteemed institute in one of the following countries are exempt: UK, Ireland, USA, Canada, Australia and New Zealand.
Please note we only accept the TOEFL Test, the test of the International English Language Testing Service (Academic IELTS) or a Cambridge Examination Score. For Non-EU/EEA students the required English test result should be received on or before 1 February in the year of application by the International Team at the Faculty of Science. If this in any case is not possible, contact the International Team first, before applying. For EU/EEA students the deadline of submitting the test results is 1 July.
The minimum scores required on the TOEFL Test are:
Please note the TOEFL-code for the Faculty of Science of the University of Amsterdam is: 8628.
A Cambridge Examination Score with a minimum test result of C1 Advanced (CAE) A or B will also be accepted. For the C2 Proficiency test (CPE) a minimal score of C is required.
Chinese applicants are required to take an IELTS test or the TOEFL (Internet-based test only). These are the only two tests accepted by the Nuffic, which provides certificates to all Chinese students who wish to study in the Netherlands.
Please note there are some differences between the TOEFL and IELTS test. Available practice material, test dates, prices and locations differ per country.