MSc Statistics and Machine Learning, 120 credits

A white and blue winding binary code tunnel leading around a corner.

Data is the driving force behind today's information-based society. There is a rapidly increasing demand for specialists who are able to exploit the new wealth of information in large and complex systems.

Autumn 2018

Statistics and Machine Learning, Master´s Programme

Autumn 2018, Full time 100%, Linköping

Start

Autumn 2018

Place of study

Linköping

Pace of study

Full time 100%

Level

Second-cycle

Teaching form

On-Campus

Application code

LIU-91009

Degree

Master of Science (120 Credits) with a major in Statistics

Entry requirements

Bachelor's degree equivalent to a Swedish Kandidatexamen within statistics, mathematics, applied mathematics, computer sicence, engineering or a similar degree. Courses in calculus and linear algebra, statistics and programming are also required.

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

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 programme, how the applicant’s academic background is related to the contents of the programme and how the applicant’s academic background matches to the specific program requirements. If there are courses in the applicant’s transcripts that match to 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 programme’s specific requirements or to the programme contents).

Tuition fees

190000 kr - NB: Applies only to students from outside the EU, EEA and Switzerland..

The programme focuses on modern methods from machine learning and database management that use the power of statistics to build efficient models, make reliable predictions and optimal decisions. The programme provides students with unique skills that are among the most valued on the labour market.

The rapid development of information technologies has led to the overwhelming of society with enormous volumes of information generated by large or complex systems. Applications in IT, telecommunications, business, robotics, economics, medicine, and many other fields generate information volumes that challenge professional analysts. Models and algorithms from machine learning, data mining, statistical visualisation, computational statistics and other computer-intensive statistical methods included in the programme are designed to learn from these complex information volumes. These tools are often used to increase the efficiency and productivity of large and complex systems and also to make them smarter and more autonomous. This naturally makes these tools increasingly popular with both governmental agencies and the private sector.

The programme is designed for students who have basic knowledge of mathematics, applied mathematics, statistics and computer science and have a bachelor’s degree in one of these areas, or an engineering degree.

Most of the courses included in the programme provide students with deep theoretical knowledge and practical experience from massive amounts of laboratory work.

Students will be given the opportunity to learn:

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

The programme contains a wide variety of courses that students may choose from. Students willing to complement their studies with courses given at other universities have the possibility to participate in exchange studies during the third term. Our partner programmes were carefully selected in order to cover various methodological perspectives and applied areas.

During the final term of the programme, students receive help in finding a private company or a government institution where they can work towards their thesis. There they can apply their knowledge to a real problem and meet people who use advanced data analytics in practice.

Date	2018-08-21
Time	10:15
Location	Linköping, Campus Valla building E, room Alan Turing