The Programme Specification for the 2022 intake onwards sets out five objectives (PO1–PO5) and six outcome groups (PLO1–PLO6, 13 criteria). Students follow their own intake documents. The 2026 update applies once formally issued.
Foreign-language exit standard: level 4/6 of the Vietnamese framework (≈ B2). The full wording of the outcomes, the objective–outcome matrix and the course–outcome matrix are in the programme documents (Documents by cohort).
One credit equals 2 ECTS (University conversion rule), 120 ECTS for the whole programme. The foreign-language course is a prerequisite and is not counted in the GPA.
The bar illustrates track 1; the table gives the allocation for all three tracks.
| Track | General | Core (compulsory) | Electives | Research / Internship | Thesis / Project | Total |
|---|---|---|---|---|---|---|
| Track 1 — research | 3 | 16 | 12 | 14 (research project) | 15 (thesis) → thesis block 29 cr. | 60 |
| Track 2 — applied, with thesis | 3 | 16 | 28 | — | 13 (thesis) | 60 |
| Track 3 — applied, internship + project | 3 | 12 | 32 | 6 (internship) | 7 (graduation project) | 60 |
Track 1 requires a scientific publication or an accepted intellectual-property application before the defence; tracks 2 and 3 earn bonus marks for a publication. Details: Thesis and Graduation.
Specialist courses carry four credits (three theory and one practical), equivalent to 8 ECTS. Check the curriculum for your intake for the applicable list. English titles follow the course syllabi.
Philosophy (3 credits). Foreign Language is a prerequisite course: students must reach level 4/6; it is not counted in the GPA.
| Code | Course | Credits |
|---|---|---|
| MTH058 | Advanced Artificial Intelligence | 4 |
| MTH003 | Advanced Database Systems | 4 |
| MTH080 | Advanced Machine Learning | 4 |
| MTH074 | Mathematical Methods in Computer Science and Algorithms | 4 |
| MTH090 | Mathematics for Data Science | 4 |
| MTH055 | Big Data Mining | 4 |
| MNC | Scientific Research Methodology (compulsory for tracks 1 and 2) | 4 |
Data science
| Code | Course | Credits |
|---|---|---|
| MTH091 | Advanced Applications of Intelligent Data Analysis | 4 |
| MTH063 | Intelligent Data Analysis | 4 |
| MTH079 | Deep Learning | 4 |
Information security
| Code | Course | Credits |
|---|---|---|
| MTH092 | Advanced Cryptography | 4 |
| MTH093 | Blockchain Technology | 4 |
| MTH094 | Advanced Privacy-Preserving Data Analysis | 4 |
Natural language processing
| Code | Course | Credits |
|---|---|---|
| MTH089 | Advanced Text Corpus Mining | 4 |
| MTH020 | Advanced Natural Language Processing | 4 |
| MTH095 | Advanced Social Network Analysis | 4 |
Visual data science
| Code | Course | Credits |
|---|---|---|
| MTH096 | Advanced Digital Image and Video Processing | 4 |
| MTH085 | Advanced Computer Vision | 4 |
| MTH097 | Advanced Mathematical Methods in Visual Data Science | 4 |
Students may also take the remaining core and specialisation courses and electives of the Faculty's Master of Artificial Intelligence and Master of Information Systems programmes, as the curriculum framework allows.
MTH106 Internship: 6 credits of practice in a company, research institute or applied unit, linked to the graduation project.
Track 1: Research project (14 cr.) + Thesis (15 cr.). Track 2: Thesis (13 cr.). Track 3: Graduation project (7 cr.). See Thesis and Graduation.
Detailed syllabi (objectives, content, assessment, reading) are published by the Faculty with the curriculum framework on the Faculty website (Vietnamese).
The plan below is a model. Follow the course schedule for your intake to stay on track, as a course usually runs only once per cohort.
Scientific Research Methodology, Advanced AI, Mathematical Methods in CS and Algorithms, Advanced Machine Learning. Register a track.
Advanced NLP, Big Data Mining, Intelligent Data Analysis, Deep Learning. Track 1 starts its research project.
Specialisation courses and electives (multimedia systems, speech processing…), Philosophy. Tracks 2 and 3 register their topic.
Remaining electives (blockchain, advanced image and video processing…); thesis (tracks 1, 2) or internship and graduation project (track 3); defence.
Key milestones from the Student Handbook: track registration about three months after enrolment; track 1 registers its topic earlier (months 4–6 of year one) because the research phase is longer; tracks 2 and 3 register at the end of year one; defence at the end of year two. Your cohort's exact dates are in the Student Handbook.
Courses combine lectures, practical work, seminars, assignments and group projects linked to research or applications. Materials and submissions are managed through the Faculty learning system.
Each course is assessed by coursework and a final assessment on a 10-point scale, with weights stated by the lecturer in the syllabus. Theses and projects are graded by a committee under University procedure. Exams, appeals and retakes follow the master's training regulations.
Entry level 3/6, exit level 4/6 of the Vietnamese framework or an equivalent certificate. The list of recognised certificates and exemptions follows the announcements of the Office of Graduate Studies.
Each intake has its own curriculum framework issued by the University. Open the PDF for your intake (documents are in Vietnamese); amending decisions apply to the intakes they name.
The current Programme Specification was issued with Decision 2449/QĐ-KHTN of 16/12/2022 and applies from the 2022 intake; the Large Language Models course was added in 2024 (Decision 590/QĐ-KHTN). The framework of your own intake applies to you.