One Year Master’s Programme in Artificial Intelligence
We live in the new era of Artificial Intelligence (AI) with industrial and governmental organisations bracing themselves for the next big wave of technological and digital innovation.
The rapid adoption and diffusion of AI will result in economical and societal transformations, while introducing novel scientific methods, new business models and social practices.
AI constitutes a disruptive innovation referring to self-learning algorithms and systems that can perform tasks by mimicking human intelligence. It is used in a wide variety of industries and application domains, such as healthcare, telecommunications, manufacturing and cybersecurity. Examples of existing AI innovations include self-driving vehicles, robotic surgeons and healthcare assistants, AI game agents, voice-activated personal assistants (e.g., Alexa and Siri) and chatbots.
AI is governed by the combination of core sciences, such as machine learning, natural language processing, big data analytics, human-computer interaction, decision making, philosophy, law and ethics. Despite the fears that naturally follow the rise of such technology, AI can undoubtedly function as the catalyst that can ensure human prosperity and equality, while fostering societal values.
This program will provide you with:
- fundamental knowledge,
- theoretical principles and technical skills for designing,
- developing and applying AI algorithms and
- methods for solving concrete scientific and societal problems,
- while complying to ethical and legal boundaries.
This one-year master’s programme is directed towards students with a technical background in computer and systems sciences.
You will find detailed course information, list of course literature, schedule and start date at courses and timetables. Select semester in the drop-down menu and search by course name.
Principles and foundations of artificial intelligence 7,5 credits
This course will introduce basic principles and foundations of AI starting by searching and problem solving and then moving on to cognitive computing and reasoning. Key concepts of machine learning, deep learning and reinforcement learning will also be covered. Finally, the course will touch upon the legal and ethical considerations of AI.
Data mining in Computer and System Sciences 7,5 credits
As data is becoming more and more readily available, the need to analyse and make use of these large amounts of data is rapidly growing. Data mining deals with techniques that can find interesting and useful patterns in large volumes of data. This course covers basic concepts, techniques and algorithms in data mining combined with hands-on experimentation.
Big Data with NoSQL Databases 7,5 credits
The course discusses the motivations behind the development of Big Data and the technologies developed to handle the properties of Big Data. These can usually not be handled by traditional database management systems due to the volume, variation and speed of the data with which they are generated. Alternative forms of representation of data have therefore evolved within the NoSQL framework. The course addresses different approaches to NoSQL within Hadoop, which is a modular framework that allows distributed storage and analysis of large amounts of data. The course covers different data sources and types of data including streaming data. The course also deals with predictive modelling with large amounts of data and gives examples of some typical applications.
Empirical Research Methodology for Computer and Systems Sciences 7,5 hp
Elective courses with specialization in artificial intelligence 15 hp
Master thesis 15 hp
How to apply
Selection processAdditional eligibility criteria
The selection of students is based on grades of academic courses.
This means that you don’t have to submit recommendation letters or motivation letter when applying to this specific programme.
Required supporting documentation
Along with your supporting documents at univeristyadmissions.se, you are required to submit a separate form with a list of proof of specific entry requirements. Download the form below.
- List the courses, from your uploaded transcript of courses, that you want to use to meet the specific requirements for this programme.
- Submit links to the course description and learning outcomes (and/or objectives), of each course, stated on your University’s website. Or link to an official descriptive document of the course (for example a pdf).
- Upload the form along with your supporting documentation at universityadmissions.se
- This form is for specific requirements only, to assist the admission board to navigate in your uploaded supporting documents. You need to submit all your supporting documentation, including general entry requirements (Bachelor’s degree, English proficiency etc.), as instructed, at universityadmissons.se
- The department is not able to give advance notice regarding special requirements. Please, apply via universityadmissions.se and upload documents for the admission board to review.
Courses that meet the requirements
Special requirements for this programme: 22,5 credits in programming and 7,5 credits in databases or equivalent.
In order to fulfil the specific entry requirement of 22,5 credits/ECTS in programming and 7,5 credits/ECTS in databases, you need to have obtained at least 30 credits (in total) in these subjects, either as part of your previous education or independent courses.
Please note again that the course needs to involve code implementation so courses which only include mark-up languages (i.e. HTML, XML), style sheet languages (i.e. CSS) are not counted in.
The database-related courses are acceptable as long as they involve implementation and not only using the database (i.e. queries).
Note: If you wish to include independent courses, remember that they need to be offered by an accredited University. Courses offered by online learning platforms (Coursera, Udemy etc.) are not counted in.
Download form: Specific entry req. Artificial Intelligence (141 Kb)
Graduates will qualify for positions such as business intelligence developer, data science or machine learning engineer, in several public and industrial sectors, such as banking, insurance, telecommunications, agent-oriented software companies and governmental organisations.