Statistics and Machine Learning
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
- ECTS transcript – If you are still completing your Bachelor’s degree, upload an ECTS transcript showing the subjects you have studied, your grades, and the number of credits awarded. Your Bachelor’s diploma must be uploaded later, as soon as you receive it.
- Bachelor’s diploma – If you have already graduated, upload your Bachelor’s diploma. A separate ECTS transcript is not required.
Note: Your Bachelor’s degree must be in the same or a related field as the Master’s programme you are applying for. The university will check whether you have completed enough relevant subjects and ECTS credits.
Important! View the full requirements on the university’s official website. Requirements may vary depending where you completed your bachelor education.
Note:
- A motivation letter and a recommendation letter are NOT required documents when applying to the programmes at this university.
Applicants have to prove their English language skills either by an internationally recognized language test or upon previous studies. Usually English language skills can be proved by the following documents:
- TOEFL iBT*
- IELTS Academic
- A Cambridge English qualification
- Higher education degree completed in English
- Secondary education completed in English in a country or school qualification accepted by the university
- An English subject and grade on your secondary school leaving certificate that meet the university’s requirements
- Other
Important: Accepted methods and minimum results depend on study programme and the country where you completed your education. Check the official requirements for your chosen study programme.
*Use the Kastu promo code LTU1000102 when registering for TOEFL iBT and receive a 5% discount.
Selection will be based on academic merits.
Bachelor's degree within statistics, mathematics, applied mathematics, computer science, engineering or a similar degree. Completed courses with passing grade in following subjects:
- calculus
- linear algebra
- statistics
- programming
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.
