MSc in Statistics and Machine Learning

Algemeen

Beschrijving van opleiding

  • Start: Autumn 2020
  • Place of study: Linköping
  • Level: Second cycle

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 program 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 program 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 program 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 analyzing the frequency of crisis-related words in financial media and internet forums
  • improve directed marketing by analyzing 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 details

The program 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 program.

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 program.

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.

Career opportunities

A specialist in high demand

Demand is increasing rapidly for specialists able to analyze 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 program the ideal background for future research. Many of the program’'s lecturers are internationally recognized researchers in the fields of statistics, data mining, machine learning, database methodology, and computational statistic.

Entry requirements

Bachelor's degree equivalent to a Swedish Kandidatexamen within statistics, mathematics, applied mathematics, computer science, engineering or a similar degree. Completed courses with a passing grade in the following subjects: 

  • calculus 
  • linear algebra 
  • statistics 
  • programming

English corresponding to the level of English in Swedish upper secondary education (English 6/B). Exemption from Swedish 3.

Special selection

Selection will be based on: 

Academic merits and Letter of Intent

Each applicant should, therefore, enclose a letter of intent written in English, explaining why the applicant wants to study at the program, how the applicant’s academic background is related to the contents of the program, and how the applicant’s academic background matches to the specific program requirements. If there are courses in the applicant’s transcripts that match the courses mentioned in the specific requirements, the applicant is recommended to name these courses in the letter of intent. It is also recommended that the applicant includes a description of other relevant experience into the letter of intent (job experience, project participation, etc. related to the program’s specific requirements or to the program contents). Submit your Letter of intent along with other documents to University Admissions.

Laatst bijgewerkt op jun 2020

Over de school

In close collaboration with the business world and society, Linköping University (LiU) conducts world-leading, boundary-crossing research in fields including materials science, IT and hearing. In the ... Lees meer

In close collaboration with the business world and society, Linköping University (LiU) conducts world-leading, boundary-crossing research in fields including materials science, IT and hearing. In the same spirit, the university offers many innovative educational programs, many of them with a clear vocational focus, leading to qualification as, for example, doctors, teachers, economists, and engineers. Lees Minder
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