Already such algorithms have allowed computers to answer these questions: "Where are the faces in this photo?" and "Can you recommend a movie for me to watch?" In layman's terms, the relationship between data and predictions/patterns is learnt by examining a large quantity of relevant example information. This idea has become central to the design of search engines, robots and sensor systems which process large data sets.
In this programme you will learn the mathematical and statistical foundations and methods for Machine Learning with the goal of modelling and discovering patterns from real world observations. You will also gain practical experience of how to match, apply and implement relevant techniques from the field to real world problems in a vast range of application domains.
The programme starts with compulsory courses in machine learning, artificial intelligence, computer security, an advanced course in machine learning and research methodology which provides an introduction and solid foundation to the field. From the second term the students choose courses from three areas; application domains within machine learning, applied mathematics/statistics, and computer science.
The first area describes how machine learning is used to solve problems in particular application domains such as computer vision, information retrieval, speech and language processing, computational biology and robotics. The second area gives the students the chance to take more basic theoretical courses in applied mathematics, statistics, and machine learning.The third area allows the students to deepen their knowledge in theoretical computer science and programming languages.
The programme also consist of 30 credits of elective courses which you can choose from a wide range of courses to specialize further in your field of interest or broaden to new areas within machine learning.
The final term is dedicated to a degree project.
The demand for engineers and scientists with knowledge in Machine Learning is growing as the amount of data in the world increases. After graduation you can pursue careers, for example as, Software Developer, Deep learning Engineer, Computer Vision Engineer, Data Analyst, Software Engineer, Quantitative Analyst, Data Scientist, and Systems Engineer in companies as Dice, Logitech, Google, and McKinsey in for example Sweden, Switzerland, Germany, China, India, and the US.
This Master's programme is also a suitable basis for work in a research and development department in industry, as well as for a continued research career, and PhD-studies.
For more information please visit the programme website.
You can apply until:
Always verify the dates on the programme website.
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Programme specific admission requirements
A Bachelor’s degree, or equivalent, corresponding to 180 ECTS credits, with a level in Mathematics and Computer Science equal to, or higher than, that of the following courses at KTH:
In addition to the general documents required, the following set of documents is required for the Master's programme in Machine Learning:
The living costs include the total expenses per month, covering accommodation, public transportation, utilities (electricity, internet), books and groceries.
Every year a limited number of KTH Scholarships are awarded based on academic excellence. Recipients of the KTH Scholarship will have their tuition fee waived for the first and the second year, provided that the study results during the first year are satisfactory.
Applications for the KTH Scholarship for studies beginning in autumn will be open December 1 - January 15.
StudyPortals Tip: Students can search online for independent or external scholarships that can help fund their studies. Check the scholarships to see whether you are eligible to apply. Many scholarships are either merit-based or needs-based.
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