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
- computational astrophysics, biology, and medicine,
- interactive technologies,
- information retrieval,
- information visualization,
- neuroinformatics, and
- social-network analysis.
The major in Machine Learning, Data Science and Artificial Intelligence (Macadamia) covers a wide range of topics in modern computational data analysis and modelling methodologies.
A Macadamia graduate
- is able to formalize data-intensive problems in data science and artificial intelligence in terms of the underlying statistical and computational principles.
- is able to assess the suitability of different machine learning methods for solving a particularly 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.
- understands the opportunities that machine learning offers in data science and artificial intelligence.
The graduates of the Macadamia major have an excellent background for pursuing an academic career within the fields of machine learning, data science and artificial intelligence, as well as in the industry applying those techniques.
Typical job titles of recent graduates include Analyst, Analytics Engineer, Data Analyst, Data Scientist, DevOps Engineer, Machine Learning Software Engineer, PhD student, Research Assistant, Software Developer, Software Engineer.
Examples of companies our recently graduated alumni work for: 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.
Graduates of the programme will graduate with a Master of Science (Technology) degree (diplomi-insinööri in Finnish).
The programme qualifies for doctoral studies (Doctor of Science in an applicable field).
Language of instruction
The language of instruction is primarily English, and the programme can be completed entirely in English. Some courses can be taken in Finnish or Swedish.
The tuition fee is €15 000 for non-EU/EEA students.
Content of the studies
The Macadamia major covers a wide range of topics in modern computational data analysis and modelling methodologies.
The major also offers a competitive doctoral track where a limited number of top students can be admitted. Students selected to the doctoral track can have their studies tailored towards pursuing PhD studies and can start working towards a PhD in one of the department’s research groups already during their Master studies. Applicants are asked to indicate their interest in entering the doctoral track in the application form.
Structure of the studies
The Master's degree (120 ECTS) is composed of studies in major (55-65 ECTS), elective studies (25-35 ECTS), and Master's thesis (30 ECT).
The elective studies can consist of additional major courses, optional minor, multidisciplinary courses, or studies abroad. Students can select their minor either from the other majors in the Master's Programme in Computer, Communication and Information Sciences or from the other Master’s programmes offered by Aalto University.
Please note that there may be changes to the curriculum for 2020-2022. The new curriculum will be published in April-May 2020.
Students are required to complete a Master's thesis, which is a research assignment with a workload corresponding to 30 ECTS credits. The topic of the thesis is agreed upon by the student and the supervising professor. Master's theses are typically written for a company or for one of the research projects of the department(s) in question.
The study environment in the programme is strongly international and studies are conducted in multicultural groups. The School of Science offers diverse possibilities for student exchange all over the world. Exchange studies can be included in the degree e.g. as an international minor. Machine Learning, Data Science and Artificial Intelligence students have also the opportunity to take the second-year studies at EURECOM in Sophia Antipolis, France, and complete a double degree graduating from both Aalto University and EURECOM.
Other possibilities for developing one’s global competence include conducting practical training abroad, taking a summer course abroad or acting as a tutor for first-year students.
Co-operation with other parties
In the field of Macadamia, there is close collaboration in teaching and research between Aalto University and the University of Helsinki in the form of joint activities within the Finnish Center for Artificial Intelligence (FCAI) and the Helsinki Institute for Information Technology (HIIT).
The topics of the major are linked to ongoing research focus areas in the Department of Computer Science in the School of Science at Aalto University.
Programme-specific admission requirements
Applicants to the programme must meet the general eligibility and language requirements that are common to all Master's programmes in the field of science and technology.
Admission criteria to the 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 in the required areas.
Required background for the Machine Learning, Data Science and Artificial Intelligence (Macadamia) 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 databases, the theory of computing, computer networks, software engineering).
Knowledge of the following areas is considered an advantage:
- additional knowledge of mathematical methods (important).
- stochastic methods, advanced probability theory and statistics.
- artificial intelligence, machine learning, and data mining.
- computational modelling and data analysis.
- signal processing.
- big data 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)
- Study success: grades achieved and pace of studies
- Recognition and quality of the applicant’s home university
- Motivation and commitment to studies in the programme
- Other relevant achievements (work experience, publications, Junction Hackathon competition or other programming competition wins, etc.)
- Language proficiency
During the evaluation of eligible applications, the applicants’ 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 listed above.
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. This means that the applicant has consistently achieved the best grades throughout the degree studies (very high weighted average grade or GPA). The recognition of the applicant’s home university affects the final interpretation of academic performance.
The minimum GPA for applicants from Finnish universities of applied science is 4.0. Applicants with a 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.
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.
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.
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. If the applicant already has a valid study right leading to a Master's degree in the admission target in question, the applicant cannot be readmitted without special reasons.
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