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

Linköping University


En Linköping (Sweden)

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    Linköping (Sweden)

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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

Información importante

¿Qué objetivos tiene esta formación?: Knowledge and understanding
For a Degree of Master (120 credits) the student shall

demonstrate knowledge and understanding in Statistics, including both broad knowledge of the field and a considerable degree of specialised knowledge in certain areas of the field as well as insight into current research and development work, and
demonstrate specialised methodological knowledge in Statistics.
Specialized knowledge in machine learning shall include modern powerful techniques for classification and regression, prediction, methods for statistical simulation and optimization, Bayesian methods and methods for analysis of large databases.

¿Esta formación es para mi?: 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.

Requisitos: Bachelor's degree equivalent to a Swedish Kandidatexamen 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

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Linköping (Sweden)
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16 ago 2021 Inscripciones abiertas


  • Database
  • Database training
  • Statistics
  • Financial
  • Financial Training
  • Introductory
  • Machine Learning
  • Computer Science
  • Machine
  • Synergy
  • Obligatory



The programme is organized as an education in data analytics that is relevant in different application areas. The profile in data analytics is created as a synergy of courses in statistics, machine learning and computer science. The programme comprises introductory, obligatory, complementary, profile courses and a master thesis.

Introductory courses are offered to prepare the students for the programme’s  other courses. Obligatory courses contain theoretical and practical tools that are necessary for solving various analytical problems. Profile courses are courses in Statistics that include models and methods which give a deeper probabilistic understanding of machine learning and data analysis. Complementary courses have diverse nature connected to statistics or machine learning and directed towards a specific application area or an advanced methodological domain. During semester 3, a possibility to exchange semester is given.

Master thesis covering 30 ECTS makes it possible for the students to apply their theoretical and practical knowledge in order to solve a relevant practical data analysis problem or going deeper into a research-oriented project.

The heading ‘Curriculum’ contains a list of courses included in the programme. The course syllabuses for these describe in more detail the contents, teaching and working methods, and examination.

Semester 1 (Autumn 2021)

  • Advanced Academic Studies
  • Statistical Methods
  • Advanced Programming in R
  • Visualization
  • Machine Learning

Semester 2 (Spring 2022)

Preliminary courses

  • Big Data Analytics
  • Neural Networks and Learning System
  • Web programming
  • Introduction to Python
  • Advanced Data Mining
  • Deep Learning
  • Bayesian Learning
  • Multivariate Statistical Methods

Semester 3 (Autumn 2022)

Preliminary courses

  • Database Technology
  • Probability Theory
  • Decision Theory
  • Research Project
  • Time Series and Sequence Learning
  • Text Mining
  • Advanced Machine Learning
  • Visualization
Semester 4 (Spring 2023)

Preliminary courses
  • Master Thesis in Statistics

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

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