Master's Programme in Computer, Communication and Information Sciences - Machine Learning and Data Mining

Degree:

Master of Science (Technology).
More information.

ECTS:

120 ECTS

Field of Study:

Technology and Engineering

Duration:

2 years, full-time

Eligibility:

An appropriate Bachelor´s degree or an equivalent qualification.

Tuition fees & scholarships:

Yes, for non-EU citizens.
More information

Language of Instruction:

English
More information.

Organising school/s:

School of Science

Machine Learning and Data Mining (Macadamia), photo Khalil Klouche

Do you want to build the future of intelligent machines?

Machine learning is one of the strong points of Aalto University. This solid education in modern computational data analysis gives you excellent opportunities for a career in research institutions or in the private sector in the rapidly developing fields of machine learning, data science, and artificial intelligence.

The methods of machine learning and data mining are applicable and needed in a wide variety of fields ranging from process industry to data science. Recent spearhead application areas include

  • bioinformatics,
  • computational astrophysics, biology, and medicine,
  • interactive technologies,
  • information retrieval,
  • information visualization,
  • neuroinformatics, and
  • social-network analysis.

Machine Learning and Data Mining (Macadamia) is one of the eight majors offered in the Master's Programme in Computer, Communication and Information Sciences (CCIS).

Macadamia Doctoral Track

In addition to normal Master’s programme, Macadamia will be offered as a doctoral track in the academic year 2018-2019. Doctoral track means that some of the students selected to the Macadamia major can have their studies tailored towards a research career and can start working towards PhD in one of the department’s research groups already during their Master studies. The applicants are asked to indicate their interest in entering the doctoral track in their application's motivation letter. Out of those applicants who have in their motivation letter indicated their interest in entering the doctoral track, the best ones will be interviewed.

Learning outcomes

The major in Machine Learning and Data Mining (Macadamia) covers a wide range of topics in modern computational data analysis and modelling methodologies.

A Macadamia graduate

  • is able to formalize data analysis problems in terms of the underlying statistical and computational principles;
  • is able to assess the suitability of different machine learning methods for solving a particular new problem encountered in industry or academia, and apply the methods to the problem;
  • can interpret the results of a machine learning algorithm, assess their credibility, and communicate the results with experts of other fields;
  • can implement common machine learning methods, and design and implement novel algorithms by modifying the existing approaches;
  • understands the theoretical foundations of the machine learning field to the extent required for being able to follow research in the field.

The studies (120 ECTS credits) consist of a major subject studies (55-65 ECTS), Master´s thesis (30 ECTS), and elective studies (25-35 ECTS). See detailed course data >>

Career opportunities

The graduates from the Machine Learning and Data Mining major have an excellent background for pursuing an academic career within the fields of machine learning and data science, as well as in industry applying those techniques.

Typical job titles of recent graduates include e.g. Analyst, Analytics Engineer, Data Analyst, Data Scientist, DevOps Engineer, Machine Learning Software Engineer, PhD student, Research Assistant, Software Developer, Software Engineer, ...

Our recently graduated alumni work e.g. in the following companies: Accenture, Aureus Analytics, Discover Financial Services, Elsevier, Jongla, Omniata Inc, Sanoma, Verto Analytics,…

Our recently graduated alumni are PhD students in the following universities: Aalto University, Brown University, Carnegie Mellon University, French Institute for Research in Computer Science and Automation (Inria), Télécom Paris Tech, University of Bristol, University of California - Santa Cruz, University of Iowa, University of Surrey,…

Tuition fees and scholarships

Non-EU/EEA students selected to the Macadamia programme will be charged tuition fees. They are eligible to apply for scholarships awarded from the Aalto University Scholarship programme. See further information >>

The Department of Computer Science invites the exceptionally qualified Master's students of all nationalities to join the departments’ Honours programme. Students admitted to the Honours programme are associated to one of the department's research groups, and will have the opportunity for part-time, research-related employment during the semesters. All selected 1st year students are automatically considered for the Honours positions, and there is no separate application process. Students selected to the Honours programme will be contacted individually.

Admission requirements

General admission requirements

The general eligibility requirements are the same for all Master's programmes in the field of science and technology. Please see also the requirements concerning language skills.

Applications that fulfill the general eligiblity requirements are sent to the academic evaluation described in more detail below.

Programme-specific admission requirements

The joint admission criteria to the Computer, Communication and Information Sciences programme is a high quality Bachelor’s degree in computer science, software engineering, communications engineering, or electrical engineering. Excellent candidates with degrees in other fields such as information systems, engineering, natural sciences, mathematics or physics will be considered if they have sufficient studies and proven skills and knowledge according to the requirements.

The required background for the Machine Learning and Data Mining major includes sufficient skills in:

  • mathematics (particularly important are linear algebra, calculus, probability theory, statistics, and discrete mathematics)
  • computer science (in particular good programming skills, data structures and algorithms. Also other courses, such as: data bases, theory of computing, computer networks, software engineering)

Knowledge of the following areas is considered an advantage:

  • additional knowledge of mathematical methods
  • stochastic methods, advanced probability theory and statistics
  • artificial intelligence, machine learning, and data mining
  • computational modelling and data analysis
  • signal processing
  • big data applications

Learn more about the academic evaluation of applications

The student selection process is competitive and the best applicants are selected according to the following evaluation criteria:

  • Content of the previous degree(s)
    • The contents of the applicant’s previous degree(s) are evaluated based on the courses available on the official transcript of records and the course descriptions submitted. The applicant is expected to have completed sufficiently studies in the major-specific subject areas (see above). Relevant work experience, professional certificates and/or online courses are judged case-by-case, but they do not, in general, compensate for the university level studies that include also the theoretical foundations of the required subjects.
  • Study success: grades achieved and pace of studies
    • The applicant’s study success will be evaluated based on the grade point average (GPA) and results in key courses. Very good previous study success is expected. The minimum GPA for applicants from Finnish universities of applied science is 4.0. Applicants with GPA below the limit cannot be admitted unless they have other exceptional qualifications. Programme’s courses or equivalent courses completed in open university or as non-degree studies with excellent grades may support the application.
  • Recognition and quality of applicant’s home university
    • The recognition of the applicant’s home university affects the final interpretation of the previous academic performance.

During the evaluation of eligible applications, the applicant’s previous study success and contents of the previous degree(s) are checked first. Only the applications who pass this preliminary evaluation will be evaluated against the full set of criteria.

  • Motivation and commitment to studies in the programme
  • Other relevant achievements (work experience, publications, etc.)
  • Recommendations
  • Language proficiency

In the final phase of the academic evaluation, the applicants who passed the preliminary evaluation, are ranked and the best applicants are selected. The programme does not have a minimum quota to be fulfilled, and not all eligible applicants will necessarily be admitted. The selection process is paper-based.

Studies in the Master’s programme should provide genuinely new knowledge for the applicant. If the applicant already has a Master’s degree, the motivation letter should clearly indicate why another Master’s degree is necessary. In most cases, non-degree or e.g. open university studies are recommended instead of degree studies to complement the earlier degree or to improve one’s professional skills.

Application documents

In addition to the compulsory application documents, the applicants are requested to provide the following, additional documents:

  • at least one original recommendation letter (preferably academic)
  • course descriptions of courses taken in relevant subject areas (see the subject list above)
  • work certificates and certificates of other relevant achievements
  • copies of any publications
  • official transcript of records for other university studies which are not included in the mandatory part of the application
  • GRE or GMAT test results, if available

The application should explain full educational history of the applicant.

Applicant’s motivation letter (compulsory part of the online application form) should be written in English. Also additional application documents described above (recommendations letter(s), course descriptions, work or other certificates, and publications) should preferably be submitted in English. If some other language than English, Finnish or Swedish is used in them, the applicant must provide precise, word-for-word translations of them.

Contact information

Location: Aalto University School of Science, Otaniemi Campus, Espoo

Contact:

  • For enquiries regarding the application process, compulsory application documents or language requirements, please read instructions first. If you cannot find an answer to your question, contact Aalto University Admission Services at admissions [at] aalto [dot] fi.
  • For enquiries regarding the contents of studies or additional application documents, please contact Master Admissions at the School of Science at masters-sci [at] aalto [dot] fi.

Page content by: | Last updated: 30.08.2017.