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Planning your MSc programme

Master's students can use the information below to plan their programme.

  1. Choosing courses
  2. Course schedule per specialisation
  3. Combining Master's Programme
  4. Thesis planning guide
  5. Research Seminars

Choosing Courses

The AI master's programme provides more possibilities to specialise and go in-depth with an interesting subject than during the bachelor's programme. Courses are more specific and challenging. Therefore, it is important that you select the courses that you find both interesting and fun to do.

  • Academic load: We advise you to take a maximum of 18 EC per period (15 EC per period is a normal load) to prevent high workload. Furthermore, some courses include a big project at the end of the course (see course guide for more information). Planning multiple courses with a final project or with the thesis in the same period might also result in a high work load.

  • Each specialisation has a number of compulsory courses, some of which are also compulsory for every specialisation (core). This is because it is important for every AI master student to have the same academic foundation.

  • Specialisation Choice courses: The second selection is choosing from a list of courses that are pre-selected to provide depth of knowledge within the specialisation.

  • Restricted Choice courses: The third selection allows you to either deepen your knowledge within your chosen specialisation by taking additional courses, or broaden your knowledge of AI topics by selecting courses from outside your specialisation (restricted choice).

  • Free Choice courses: The final selection step is the option to follow other courses, even courses outside the AI-curriculum. Because you have a lot of freedom to create your own curriculum, it is important that you plan everything in advance. Course activities might overlap or may all fall in the same period. If you are unsure about the suitability of an elective course, please contact the Examining Board at excieai@ai.ru.nl

  • Capita Selecta (Free Choice option) courses: A capita selecta (Latin for "selected chapters") is an individual study component that can be taken as an elective in the AI programme. The purpose of the Capita Selecta is to allow students to gain knowledge or experience in an area of study that is not covered by regular courses. The procedure must be strictly followed and is explained in detail in MKI10 (3EC) and MKI20 (6EC).

Course Schedule per Specialisation

An overview of each specialisation and track that can help you to select courses for each period in your programme can be found in the following schemes:

Combining Master's Programmes

Starting in September 2018, the AI department and Data Science (MSc Computing Science) have agreed that both AI specialisations can be combined with Data Science (DS) in a Combined Master’s Programme. AI and Cognitive Neuroscience (CNS) already had an agreement that allows any AI specialisation to be combined with any CNS track. Cognitive Computing + Perception Action & Control, or Brain Networks and Neuronal Communication, or Intelligent Technology + Perception Action & Control, are the most convenient combinations. Read more about the Combined Master’s Programme opportunities here.

Requesting a Combined Master’s Programme should be done by making use of the forms in the specific Master Spaces in Brightspace. Clear instructions can also be found there.

Research Seminars

A Research Seminar is a more or less free-form educational activity where a number of master's students and DCC researchers (PhDs, Post-docs or senior faculty) together define a topic and goals, under supervision of an AI staff member. The Research Seminar provides hands-on training in scientific research and allows participants to become acquainted with the state-of-the-art in a specific topic. In previous years, staff members have organized seminars on deep learning, neuromorphic computing, and developmental robotics. In contrast to a Capita Selecta project, which are individual projects, research seminars are based on a community. This can be diverse, e.g., group projects (such as submitting a demonstration to the annual BabyBot competition) or individual projects (such as running a network simulation on the BrainScaleS system) with regular group meetings. Ideally, the Research Seminar has a tangible outcome, such as a paper, a piece of software, a demonstration, etc.

Students can contact individual AI staff members with suggestions, but note that the time that staff members can spend on activities outside the regular curriculum is limited.