Make big sense of big data
Big data and data-driven methods are at the centre of modern computer science, statistics and computer engineering and hold great promise for the future.
Experts on analyzing data are needed for solving challenging data driven problems such as understanding of text documents, conversation and social media; creating intelligent search engines; finding data-driven insights into phenomena of society, economy and culture; creating data-driven solutions for medical and biological problems; and enabling self-driving cars and autonomous robots.
Tampere University offers three related tracks that involve analysis, modeling, prediction, and computation with big data: Data Science (M.Sc.) and Statistical Data Analytics (M.Sc.) tracks focus on computational and statistical algorithms for data mining and machine learning, with differing emphases, Machine Learning (M.Sc. Tech) track focuses on engineering accurate predictive machine learning models.
Machine Learning is an engineering programme, where the particular emphasis is on implementing machine learning algorithms with an application-oriented focus, with applications related to imaging, audio or other sensor data. We study both classical and novel deep learning models as well as their software and hardware implementations. Machine learning is the core technology of artificial intelligence (AI) and currently one of the hottest topics in information technology job markets.
Machine Learning (M.Sc. Tech) track is one of the seven tracks in the Master’s degree programme in Computing Sciences, a new degree programme starting in August 2020.
The machine learning courses cover both theory and practice. They address a wide spectrum of machine learning techniques in classification, regression, unsupervised learning and reinforcement learning (robotics) settings. We are committed to keep the module up-to-date with the rapid advancements made in the field. We use modern tools, including the famous scikit-learn and Tensorflow libraries. On some courses, we also organise machine learning competitions where students solve research problems.
Tampere University offers students the opportunity for a broad, cross-disciplinary education. A rich variety of minor studies and supplementary courses are available as well as the opportunity to concentrate purely on major studies. In fact, each degree is a unique combination of studies that the individual student has found most interesting.
After completing the programme, you will be qualified to pursue a wide range of career opportunities in different fields of technology. The skills of data-driven problem solving are in high demand. This can be seen, for example, in surveys that demonstrate that data and machine learning engineers are among the highest-paid of all professional programmers. Tampere is a vibrant industrial hub for various types of companies with needs for machine learning experts.
Graduated Master’s of Science typically find employment in research, design, development, production and operating tasks, or commercial and administrative tasks relating to the field, without excluding abilities to work as a researcher, teacher or manager.
Following the successful completion of the programme, graduates are also eligible to apply for entrance to doctoral programmes in Finland and abroad.
Eligibility criteria in the Master’s degree programmes consist of two parts: Tampere University’s general eligibility criteria, and programme-specific eligibility criteria, which specify and/or add information to the general criteria. You must meet both to be considered eligible. Applicants who do not meet both the general eligibility and the programme-specific eligibility criteria will not be selected as students in Tampere University.
To be eligible to apply to a Master’s degree programme at Tampere University, you must have
- Bachelor’s degree – nationally recognized first cycle degree
- which corresponds to at least 180 ECTS (European credits) or to three years of full-time study
- from relevant field for the Master’s degree programme that you’re applying to
- from an accredited institution of higher education
- AND a good command of the English language for academic purposes. For more information, please see Language requirements.
A Bachelor’s degree completed in a university outside Finland must provide eligibility for university-level master’s degree studies in the country in which it was awarded.
Applicants in their final final year of the Bachelor’s degree are eligible to apply if the degree will be completed by 31 July 2020. In this case, they may be accepted conditionally. Please see special instructions for applicants in the final year of the Bachelor’s degree.
Applicants with a Master’s degree may be admitted only well-founded reasons and only if they are able to show that the new degree at Tampere University provides the applicant with significant new knowledge and skills.
Programme-specific eligibility criteria
The previous degree should be from a field closely related to the Master’s programme you are applying to. In the Master’s programme in Computing Sciences, Machine Learning (MSc tech) track, the previous degree has to be in one of the following (or highly related) fields:
- computing, information technology, computer science, electrical engineering, software engineering or applicable field of engineering with proficiency in mathematics, physics and elementary programming skills.
Applicants who have completed their degree in Finland must meet the minimum cumulative GPA set by the programme to be eligible. The minimum is the same as in the admission to the same Master’s degree programme offered in Finnish.
|Minimum GPA for bachelor’s degree completed in a Finnish University of Applied Sciences (AMK)||Minimum GPA for bachelor’s degrees completed in a Finnish university (yliopisto)|
When counting the cumulative GPA weighted by ECTS only the courses (incl. thesis) with numeric grade value are taken into account. Applicants in their final year of Bachelor’s degree must meet the minimum cumulative GPA when applying and after graduation.
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