Statistics and Machine Learning, Master's Programme, 120 credits

Unleash the power of data and statistics to make the right decisions happen. We integrate statistical modelling and analysis with machine learning, data mining and data management to give you unique skills.

Statistics and Machine Learning, Master´s Programme - First and main admission round

Autumn 2020, Full-time, Linköping

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Statistics and Machine Learning, Master´s Programme - Second admission round (open only for Swedish/EU students)

Autumn 2020, Full-time, Linköping

Statistics and Machine Learning, Master´s Programme - First and main admission round

Autumn 2021, Full-time, Linköping

Apply

Statistics and Machine Learning, Master´s Programme - Second admission round (open only for Swedish/EU students)

Autumn 2021, Full-time, Linköping

The rapid development of information technologies has overwhelmed society with enormous volumes of information generated by large or complex systems from telecommunications, robotics, medicine, business and many other fields. This master’s programme meets the challenges of learning from these complex volumes by means of models and algorithms from machine learning, data mining and other computer-intensive statistical methods. By joining us, you will increase the efficiency and productivity of the systems and make them smarter and more autonomous.

Learn to make reliable predictions

The programme focusses on modern methods from machine learning and database management that use the power of statistics to build efficient models and make reliable predictions and optimal decisions. You will gain deep theoretical knowledge as well as practical experience from extensive amounts of laboratory work. If you want to complement your studies with courses at other universities, you can participate in exchange studies during the third semester.

Depending on your interests, you will work towards your thesis at a company, a governmental institution or a research unit at LiU. There you can apply your knowledge to a real problem and meet people who use advanced data analytics in practice or you can go deeper into the research.

This programme is for you if you aspire to learn how to:

  • improve the ability of a mobile phone’s speech recognition software to distinguish vowels in a noisy environment
  • provide early warning of a financial crisis by analysing the frequency of crisis-related words in financial media and internet forums
  • improve directed marketing by analysing shopping patterns in supermarkets’ scanner databases
  • build an effective spam filter
  • estimate the effect that new traffic legislation will have on the number of deaths in road accidents
  • use a complex DNA microarray dataset to learn about the risk factors of cancer
  • determine the origin of an olive oil sample with the use of interactive and dynamic graphics

Syllabus and course detailsShow/Hide content

The programme runs over two years and encompasses 120 credits, including a thesis.

The introductory block of courses contains a course in basic statistics that is recommended for students with a background in computer science or engineering, and a course in programming that is recommended for students having a degree in statistics or mathematics. The courses Machine learning, Advanced Data Mining, Deep Learning, Big Data Analytics, Computational Statistics and Bayesian learning constitute the core of the programme.

In addition, master’s students have the freedom to choose among profile courses - aimed to strengthen students’ statistical and analytical competence - and complementary courses - that allow students to focus on particular applied areas or relevant courses from other disciplines. Opportunities for exchange studies are provided during the third semester of the programme.

To be awarded the degree, students must have passed 90 ECTS credits of courses including 42 ECTS credits of the compulsory courses, a minimum of 6 ECTS credits of the introductory courses, a minimum of 12 ECTS credits of the profile courses, and, possibly, some amount of complementary courses. The students must also have successfully defended a master’s thesis of 30 ECTS credits.

Further information

A detailed syllabus, curriculum, and information on the courses you may take can be found in our study information database via the link below. Entry requirements and tuition information can be found by selecting the drop-down ”Admission requirements” available under the Autumn 2021 tab.
Syllabus and curriculum

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A specialist in high demand

Demand is increasing rapidly for specialists able to analyse large and complex systems and databases with the help of modern computer-intensive methods. Business, telecommunications, IT and medicine are just a few examples of areas where our students are in high demand and find advanced analytical positions after graduation.

Students aiming at a scientific career will find the programme the ideal background for future research. Many of the programme’'s lecturers are internationally recognised researchers in the fields of statistics, data mining, machine learning, database methodology and computational statistic.

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