The aim of this module is to provide the essential mathematical knowledge and skills required in computer science. It develops an understanding of the mathematical principles underpinning algorithms, data structures, and the theoretical foundations of computer science, and creates the prerequisites for applying mathematical methods to solve discipline-specific problems.

The core module develops the ability to apply modern software engineering principles and processes to ensure software quality, including agile methodologies, in software project development and management. The courses in this module teach both how to develop systems (OS-level) software and how to apply appropriate architectural principles for building complex distributed systems, including peer-to-peer (P2P) and messaging protocols.

Special studies form the core of the programme, where computer science meets applied artificial intelligence. The aim of the module is to provide broad-based knowledge—specialised according to the chosen electives—covering algorithms, concepts and methods in software engineering, intelligent robotics, machine learning, data science, cryptography and computer vision. The module prepares students for independent research and development work by strengthening analytical, synthesis and critical thinking skills, and offers flexibility based on individual interests.

Free choice courses that allow students to broaden their profile, including entrepreneurship or interdisciplinary project-based learning.

A substantial research or development project addressing a complex scientific or industrial challenge. In the double degree option, supervision takes place at one of the partner universities, while assessment is carried out by a joint committee including members from both TalTech and TU/e.

To apply, you need a Bachelor’s degree or equivalent in Information Technology or a closely related field (IT, Computer Science, Software Engineering or equivalent).

In order to qualify for the studies in Tallinn University of Technology an applicant has to have at least 60% of the highest possible CGPA. If the average grade for your Bachelor's degree is below 60%, the requirement can be considered fulfilled upon the programme manager’s proposal if the applicant’s average grade from the applicant’s previous master’s studies is at least 60% of the maximum possible grade. Note that this principle can be implemented only if the CGPA of your Bachelor's degree does not fall under 50%.

In order to apply, a candidate is required to upload a Test-taker Score Report of the GRE General Test to the DreamApply application system with the necessary threshold scores:

• Verbal Reasoning at least 145
• Quantitative Reasoning at least 150
• Analytical Writing score at least 3.0

This is a prerequisite for qualifying and has to be fulfilled no later than the application deadline.

Candidates who have obtained a full Bachelor's degree (or subsequent higher education degree) in EU, EFTA or OECD member state are waived from the GRE requirement.

Please see more information about our GRE General Test requirement here, including the exact eligibility criteria for being exempted from this prerequisite and how to order the test result electronically to TalTech.

The motivation letter and CV must be submitted in English.

Assessment: 0–10 points (a positive result is 5–10 points):

• clear and correct written communication in English and overall presentation;
• what and why the applicant wants to study (motivation and goals);
• the relevance and fit of previous studies and (work) experience for the programme;
• a coherent and well-structured CV and motivation letter;
• if possible, a link to the repository of the applicant’s most complex software project or multiple projects (e.g. GitHub, GitLab).

Positive result (5–10 points):

The applicant’s motivation is clearly justified, the connection to the curriculum is meaningful and realistic, and the submitted documents convincingly demonstrate suitability and readiness for Master’s-level studies.

Negative result (0–4.99 points):

The motivation is superficial or unclear, the connection to the programme is weak or missing, prior experience is not described in an understandable way, or the documents are incomplete and/or incorrectly formatted.

The interview is conducted with the Programme Director in English and lasts approximately 15 minutes.

Assessment: 0–10 points (a positive result is 5–10 points).

Admission to the interview requires a positive result in the CV and motivation letter assessment.

Topics covered in the interview (to determine whether the threshold is met):

• Motivation for the programme – the ability to justify the choice of the programme and relate it to the applicant’s career plans (how completing the programme supports their professional development). A meaningful understanding of the knowledge and skills gained in the programme – which areas the applicant most needs to develop. Readiness and ability to complete the programme within the nominal study period.
• Awareness of core topics in computer science and artificial intelligence – the ability to bring examples of key issues/problems in computer science and applied AI and discuss them (including personal experience with relevant topics through projects, internships or work experience). Awareness of major trends in the field and an understanding of how AI solutions are applied in real systems (e.g. data quality, reliability, scalability, security, ethical aspects).
• Readiness to carry out the Graduation Project (research or development project) – the applicant’s interest and ability to formulate a potential topic or problem to solve in the Graduation Project; the level of reflection and argumentation behind the topic; understanding of possible approaches (e.g. system design, experiment, prototype, data analysis, modelling) and, if applicable, opportunities for collaboration with an industry partner or research group. English language proficiency and the ability to communicate on professional topics (the programme is taught in English).

The interview result is assessed on a 0–10 scale. The admission threshold is 5.0 points.

If the applicant does not attend the interview, the result is 0 points. The maximum result is 10 points. A negative result is a total score of 4.99 points or lower.

Interview assessment criteria 

The overall score is calculated based on the following three components. The score is an average of these components, provided that all individual scores are 3 points or higher. If any component scores less than 3 points, the overall score defaults to the lowest individual score.

Positive result 5–10 points:

1.Motivation and justification of programme choice

 The applicant’s justification is well argued and the applicant has a clear vision for their professional development.

 The applicant understands the curriculum and can explain why this programme is suitable for achieving their goals.

 Motivation is primarily driven by the desire to develop into a high-level specialist and apply the acquired knowledge in professional growth. The applicant can link the programme to prior experience and future career plans.

 The applicant has assessed their resources and ability to complete the programme within the nominal timeframe (time planning, workload, combining work and studies if needed, realistic plan).

2.Subject knowledge and field awareness

 The applicant can provide examples of key topics and problems in computer science and AI and discuss them meaningfully using appropriate terminology.

 The applicant demonstrates an understanding of how AI solutions function in real software systems and what the key challenges are.

 The applicant has clear expectations regarding the knowledge and skills they want to gain from the programme.

3.Study readiness and awareness of research/development direction

 The applicant can discuss potential Graduation Project topics and justify their interest in the chosen direction.

 The applicant can describe a realistic approach, highlight possible data sources or experimental goals, and understands what kinds of outcomes can be expected.

 The applicant can assess strengths and development needs and relate prior studies/work experience to the programme.

 The applicant demonstrates good spoken English in professional contexts and is able to understand and comment on English texts used in studies.

Negative result 0–4.99 points:

1.Motivation and justification of programme choice

 The justification is unclear or superficial and the applicant cannot convincingly explain how the programme supports professional development or future plans.

 The applicant lacks a clear understanding of the curriculum, or describes it only in general terms without meaningful links to their goals.

 The applicant has not considered their resources and ability to complete the programme within the nominal timeframe (time planning/workload is not thought through or is unrealistic).

2.Subject knowledge and field awareness

 The applicant can mention isolated topics in computer science or AI, but the discussion is one-sided and weak; there is no clear evidence of relevant experience with the curriculum topics.

 The applicant does not understand the key focus areas (computer science + applied AI) and cannot discuss practical aspects of applying AI in systems (e.g. deployment, quality, scalability, security, the role of data).

 The applicant lacks clear expectations about what they want to learn or cannot connect goals with broader developments in the field.

3.Study readiness and awareness of research/development direction

The applicant can name an area of interest, but the topic is not thought through; there is no well-argued rationale or understanding of how it could be addressed as a Graduation Project.

The applicant has superficial knowledge of methods/approaches and cannot realistically assess feasible activities and outcomes within the project.

The applicant has difficulty linking prior knowledge and skills to the programme and cannot clearly articulate the focus of their interests.

English professional communication is insufficient for studies (limited understanding and expression; uncertain use of terminology).