Introduction
The data-intensive major in Machine Learning, Data Science and Artificial Intelligence (or “Macadamia”) deals with some of the most challenging problems of the 21st century. Be it finding new solutions to tackle climate change or better understanding the causes of an epidemic, this field has an integral part to play.
Study objectives
What exactly is intelligence, and how does it evolve? What is learning and why has 'learning to learn' become a crucial skill in today’s world? Wanting to find answers to questions as plain and straightforward as these can be enough of a reason for someone to study machine learning, data science and artificial intelligence. Yet these fields also deal with some of the most challenging problems of the 21st century, making the Machine Learning, Data Science and Artificial Intelligence (or “Macadamia”) major at Aalto University an ideal study environment for someone who is motivated to get out of their comfort zone. Be it to find new solutions to tackle climate change or better understand the causes of an epidemic, artificial intelligence, data science and machine learning have an integral part to play.
A Macadamia graduate:
is able to define data-intensive problems in data science and artificial intelligence and understand their underlying statistical and computational principles.
is able to evaluate the suitability of different machine learning methods for solving a new problem encountered in industry or academia, and apply the methods to the problem.
can effectively interpret the results of a machine learning algorithm, assess its credibility, and communicate the results to 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 to be able to follow research in the field.
understands the opportunities that machine learning offers in data science and artificial intelligence.
Language of instruction
In the Macadamia major the language of instruction is English.
Tuition fees
The tuition fee is €15 000 for non-EU/EEA students per academic year. More information on Scholarships and Tuition Fees page (aalto.fi).
Content of the studies
Major compulsory courses at the beginning of the studies provide a strong foundation before further study in specific sub-areas. Students have the opportunity to dive deeper into areas such as Bioinformatics and Speech and language. There is also a range of general optional courses for students to choose from and they can also include optional courses from other majors in their study plan per agreement with a professor in charge of the major.
Doctoral Track
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 possible interest for the doctoral track in their motivation letter when applying for the Master’s programme. The best doctoral track applicants will be interviewed.
Topics
The technological and societal artificial intelligence revolution extends to all life and industries, providing us with countless new opportunities while also raising many questions about where the dangers lie. The topics in the courses touch the most recent phenomena and research findings in this revolutionary technology. To give concrete examples of the courses available, the following is a selection from the programme’s extensive curriculum:
Machine Learning: Supervised Methods (5 ECTS)
Deep Learning (5 ECTS)
Bayesian Data Analysis (5 ECTS)
Machine Learning: Advanced Probabilistic Methods (5 ECTS)
Artificial Intelligence (5 ECTS)
Methods
Aalto University’s Department of Computer Science is quickly rising in rankings and is now among the top departments in Europe. Students in the Machine Learning, Data Science and Artificial Intelligence major are provided with access to cutting edge research and guidance from leaders in the field. ​
The studies emphasise active, hands-on learning. Projects and different practical assignments are meant to engage students in active learning and encourage them to try out things for themselves instead of remaining passive recipients of the information. The faculty consists of enthusiastic and internationally acclaimed professors and researchers in the field, all contributing to an enjoyable and encouraging learning environment.
Personal Study Plan (PSP)
The Personal Study Plan (PSP) is a practical tool to define a student’s own study path, compiling an optimal selection of courses that are aligned with the student's interests and programme requirements. PSP is also a useful tool for students to keep track of their studies. At best, it shows where students are with their studies and sets concrete milestones for them to follow.
Structure of studies
Overall, the Master’s Programme in Computer, Communication and Information Sciences – Machine Learning, Data Science and Artificial Intelligence comprises a total of 120 ECTS credits. The two-year programme consists of:
Major studies (60 ECTS)
Elective studies (30 ECTS)
Master’s thesis (30 ECTS)
Career opportunities
Machine learning and artificial intelligence are disrupting virtually every business in every industry. Staying on top of this revolutionary technology is imperative for organisations seeking to maintain a competitive edge. Since the demand for AI professionals outpaces the current skilled AI engineers, the graduates of this major have limitless opportunities open for them, ranging from process industry to data science. Recent spearhead application areas include:
Bioinformatics
Computational astrophysics, biology, and medicine
Interactive technologies
Information retrieval
Information visualisation
Neuroinformatics
Social-network analysis
Typical entry-level 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. Graduates can expect to advance rapidly in their chosen career.
Examples of companies our recently graduated alumni work for: Accenture, Aureus Analytics, Discover Financial Services, Futurice, Elsevier, Jongla, Omniata Inc, Reaktor, Sanoma, Silo AI and 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), Purdue University, Télécom Paris Tech, University of Bristol, University of California - Santa Cruz, University of Iowa, University of Surrey.
Aalto University has well-established career services to support students’ employment in Finland and abroad. Thanks to the flexible curriculum, many Aalto students work already during their studies and guarantee themselves entry positions before graduation. There is also a very active entrepreneurship community at Aalto, working as a springboard for founding a company.
Internationalisation
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 and internships all over the world. Students may find themselves doing an internship in Silicon Valley or taking a summer course at one of Aalto's partner institutions. Macadamia students have also the opportunity to take their second-year studies at EURECOM, France, and complete a double degree graduating from both Aalto University and EURECOM. In addition, the Macadamia major co-operates closely with ELLIS, the European Laboratory for Learning and Intelligent Systems, which has some of the best academic institutions and scientists under its umbrella. Should the students want to become top researchers in the field, they have an excellent opportunity for that.
Aalto University is international by nature, welcoming thousands of degree and exchange students from abroad every year. These students join the diverse Aalto community not only through their studies but also through multiple free time events, celebrations and extracurricular activities around the campus. Active tutoring programs and support services work hard to help international students integrate to the Nordic culture and feel at home in Finland.
Multidisciplinary opportunities
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). The latter brings together top talents in academia, industry and public sector to solve real-life problems using both existing and novel AI. One of the current research areas centres around the opportunities that AI creates for medicine. Excellent Macadamia students can continue their studies in the Helsinki Doctoral Education Network in Information and Communication Technology (HICT).
Students can also include multidisciplinary studies to their degree by studying minor or optional courses from other fields. They also have the option to take courses from other Finnish universities via The Flexible Study Right Agreement (JOO).
Aalto University is well-known for bridging disciplines of business, arts, technology and science. The lively campus and freedom of choosing elective courses across the University bring students from different fields under one roof. This spontaneous multidisciplinary environment sparks new ideas gathers enthusiasts around them and gives birth to friendships, networks, and every so often, startups.
Programme-specific Eligibility Requirements and Evaluation Criteria
In Macadamia major, required background 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, databases)
Knowledge of the following areas is considered an advantage:
additional knowledge of mathematical methods (important)
optimization, stochastic methods, advanced probability theory and statistics
artificial intelligence, machine learning, and data mining
computational modelling and data analysis
big data applications, signal processing
theory of computation, computer networks, software engineering
Evaluation Criteria
The applicants must first meet the general eligibility requirements of Aalto University.
Applicants meeting the general eligibility criteria for master's studies are evaluated and ranked according to the evaluation criteria listed here.
Firstly, the applications are evaluated based on the following, critically important criteria.
Academic performance
Relevance of previous studies
Only the applications that meet the requirements for these criteria will be evaluated further using the criteria below:
Recognition and quality of an institution
Suitability
Other areas of competence
The best applicants will be selected based on the joint evaluation of all the above criteria. The programme does not have a minimum quota to be fulfilled, and not all eligible applicants will necessarily be admitted.
Academic performance
Admission requirements/What we look for in an applicant
The CCIS programme is looking for applicants with excellent study success in their previous studies. In study options where the number and quality of applications are high, this means that the applicant has achieved consistently excellent grades throughout the degree studies (very high weighted average grade or GPA).
The applicant’s study success will be evaluated based on the grade point average (GPA) and grades in key courses. The time spent on previous studies will also be taken into account. All the applicant’s previous university studies, including incomplete degrees and non-degree studies, will be taken into consideration when evaluating study success.
The minimum GPA for applicants from Finnish universities of applied sciences is 4.0. Meeting the minimum GPA does not guarantee admission to the programme. Applicants with a GPA below the limit cannot be admitted unless they have other exceptional qualifications. Programme’s courses or equivalent courses completed in the open university or as non-degree studies with excellent grades may support the application.
What is evaluated?
Grades of the previous degree(s) and pace of studies
Evaluated documents
Transcript(s) of records, degree certificate(s)
Relevance of previous studies
Admission requirements/What we look for in an applicant
Applicants are expected to have a high-quality Bachelor’s degree in computer science, software engineering, communications engineering, or electrical engineering. Excellent candidates with degrees in other fields including but not limited to information systems, engineering, natural sciences, mathematics or physics will be considered if they have sufficient studies in the required areas.
The required background knowledge, as well as areas of knowledge that are considered an advantage, are defined above under "study-option specific eligibility requirements".
The contents of the applicant’s previous degree(s) are evaluated based on the courses on the official transcript of records. Relevant work experience, professional certificates and online courses can also be taken into account in the evaluation, but they do not, in general, compensate for the absence of university-level studies covering the theoretical foundations of the knowledge areas.
What is evaluated?
Content and quantity of previous studies in relation to the applied study-option-specific requirements
Evaluated documents
Transcript(s) of records, course descriptions
Recognition and quality of an institution
Admission requirements/What we look for in an applicant
We expect applicants to have completed their previous degree in a high-quality university and programme. The recognition of the applicant’s home university affects also the interpretation of the grades when evaluating the academic performance.
What is evaluated?
Recognition and quality of the applicant's previous institution
Evaluated documents
International and national rankings of higher education institutions
Suitability
Admission requirements/What we look for in an applicant
The applicant should be motivated to study the chosen subject and committed to full-time studies with a plan to complete the Master’s degree in two years. We are looking for applicants who are able to express clearly the reasons for applying to the study option and describe why they would be excellent candidates for the study option.
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 one is necessary. In most cases, non-degree studies are recommended instead.
What is evaluated?
Applicant’s suitability to the study option, motivation, commitment to the studies, and written communication skills
Evaluated documents
Application as a whole including a motivation letter
Other areas of competence
Admission requirements/What we look for in an applicant
Beyond their academic record, applicants may have other experience, knowledge and qualifications that prepare them for the Master’s studies and distinguish them among their peers.
We particularly value demanding work experience in the area of the planned studies, participation in scientific research leading to publications, entrepreneurship, and special achievements such as success in competitions (e.g. Junction Hackathon).
What is evaluated?
Work experience, other qualifications and achievements
Evaluated documents
Curriculum vitae, recommendation letters, proofs of employment, publications, other certificates, GRE or GMAT results (if available).
