The Master of Applied Science in Artificial Intelligence (MAS in AI) at Madan Bhandari University of Science and Technology (MBUST) is a two-year, research-based postgraduate program that combines machine learning, computer vision, natural language processing, advanced deep learning, and applied research.
The program develops students' ability to design, implement, evaluate, and investigate artificial intelligence systems. Its curriculum integrates technical coursework, practical programming, group projects, research methods, and supervised thesis work.
Students also examine ethical issues associated with artificial intelligence and its applications in agriculture, healthcare, industrial automation, connected devices, and social media. The program connects technical study with research projects addressing practical problems and selected Sustainable Development Goals (SDGs).
MBUST offers the degree through its Artificial Intelligence academic area at Chitlang, Makawanpur District, Bagmati Province, Nepal.
| Particulars | Details |
|---|---|
| Program | Master of Applied Science in Artificial Intelligence |
| Abbreviation | MAS in AI |
| University | Madan Bhandari University of Science and Technology (MBUST) |
| Academic Level | Master's |
| Duration | 2 years |
| Academic Structure | 4 semesters |
| Total Credits | 50 |
| Thesis Credits | 28 |
| Study Mode | Full-time, research-based |
| Major Subjects | Machine Learning, Computer Vision, Natural Language Processing, Deep Learning, Data Science |
| Research Component | Group project and supervised thesis |
| Eligibility | Four-year Bachelor's degree in a relevant field with minimum CGPA 2.75/4.0 or international equivalent |
| Location | Chitlang, Thaha Municipality, Makawanpur District, Bagmati Province, Nepal |
The MAS in Artificial Intelligence combines foundational computing methods with advanced study of intelligent systems and their applications.
Students study how machine learning methods are developed, how computational models process images and language, and how artificial intelligence techniques can be applied to specific technical and organizational problems.
The degree places substantial emphasis on research. Alongside formal coursework, students complete a group project and undertake supervised thesis research in artificial intelligence or an application area.
The academic coverage extends to agriculture, healthcare, industrial automation, social media, and connected technologies. These areas provide subjects for investigating how artificial intelligence methods can be designed, tested, and evaluated for different purposes.
The program also addresses scientific communication, research literature, ethical considerations, and the assessment of technical results.
The program aims to develop theoretical knowledge, practical abilities, and research competence in artificial intelligence.
Its principal learning objectives include:
Understanding artificial intelligence, machine learning, natural language processing, and computer vision.
Applying computing tools and methods to defined technical problems.
Designing and evaluating artificial intelligence applications.
Examining ethical questions related to artificial intelligence and its use.
Reviewing and critically assessing scientific literature.
Understanding applications of artificial intelligence in agriculture, healthcare, connected devices, industrial automation, and social media.
Planning and conducting advanced technical and research activities.
Communicating research methods, findings, and technical results to different audiences.
These objectives connect computing knowledge with practical investigation and independent research.
The MAS in Artificial Intelligence consists of 50 credits completed over four semesters.
The curriculum combines core technical courses, a group project, a technical elective, compulsory non-credit courses, and thesis research.
| Academic Component | Credits |
|---|---|
| Core Courses | 15 |
| Group Project | 4 |
| Technical Elective | 3 |
| Thesis | 28 |
| Total | 50 |
Thesis research accounts for 28 credits, making it the largest academic component of the degree.
The first semester introduces machine learning, practical data science, project work, and research methods.
| Course Code | Course Title | Credits |
|---|---|---|
| AI-CR-501 | Machine Learning | 3 |
| AI-CR-502 | Practical Data Science with Python | 2 |
| AI-CR-503 | Group Project in People-Centred AI | 4 |
| AI-CR-504 | Research Methods for Intelligent Systems | 1 |
| AI-NC-505 | Development Policy | 0 |
This semester establishes the technical and research foundation for subsequent specialized study.
The second semester concentrates on computer vision, natural language processing, and a technical elective related to the student's research interests.
| Course Code | Course Title | Credits |
|---|---|---|
| AI-CR-550 | Computer Vision | 3 |
| AI-CR-551 | Natural Language Processing | 3 |
| AI-EL-561–569 | One Technical Elective | 3 |
| AI-NC-552 | Entrepreneurship and Innovation Strategies for AI | 0 |
| AI-NC-553 | Case Studies in Ethics and Fairness in AI | 0 |
The non-credit courses extend the technical curriculum into entrepreneurship and ethical questions associated with artificial intelligence.
The third semester includes Advanced Topics in Deep Learning (AI-CR-601), carrying three credits, alongside thesis research under AI-TH-699.
The advanced course examines specialized deep learning methods, while the thesis component supports sustained investigation of a selected research problem.
The fourth semester is devoted to thesis research under AI-TH-699.
The student continues the selected investigation, developing and evaluating research findings within the scope of the thesis.
Machine Learning introduces methods for constructing computational models from data.
The course includes decision-making under uncertainty, optimization, generalization, probabilistic approaches, information theory, neural networks, kernel methods, ensemble methods, and deep learning.
This course develops practical computing skills for working with different types and sizes of data.
Its content includes time-series, spatial, and text data; computational methods for large-scale analysis; data structures and algorithms; and data visualization.
Computer Vision examines computational methods for interpreting images and video.
Students study filtering, edge detection, feature extraction methods such as SIFT and SURF, object detection, image segmentation, and image and video classification.
Natural Language Processing addresses computational analysis and processing of human language.
The course covers syntax, semantics, entity recognition, relation extraction, word embeddings, dialogue systems, and large language models.
The advanced course examines specialized neural network methods.
Topics include generative adversarial networks, graph convolutional networks, graph neural networks, diffusion models, and score-based models.
Students select one technical elective relevant to their intended thesis research.
| Course Code | Technical Elective | Credits |
|---|---|---|
| AI-EL-561 | Reinforcement Learning | 3 |
| AI-EL-562 | Artificial Intelligence and Multi-Agent Systems | 3 |
| AI-EL-563 | Graphical Models | 3 |
| AI-EL-564 | AI for Agriculture | 3 |
| AI-EL-565 | Data, Algorithm and Society | 3 |
| AI-EL-566 | AI and Internet of Things | 3 |
| AI-EL-567 | Applied Health Care and AI | 3 |
| AI-EL-568 | Industrial AI and Automation | 3 |
| AI-EL-569 | Social Media Analysis for Social Good | 3 |
The elective subjects allow students to connect artificial intelligence methods with a specialized technical area or application sector.
Research begins within the taught portion of the degree through the four-credit Group Project in People-Centred AI.
The project covers software engineering approaches for artificial intelligence systems, collaborative development methods, stakeholder interaction, evaluation of technical solutions, and communication with technical and non-technical audiences.
The 28-credit thesis is a separate and more extensive research component. Students investigate a problem in artificial intelligence or apply research methods to a practical problem.
The thesis requires students to examine the research question, develop an appropriate technical approach, evaluate findings, and present the results of their work.
This emphasis on research distinguishes the MAS from a postgraduate program organized primarily around classroom examinations and taught subjects.
Applicants must have completed a four-year Bachelor's degree in Science, Engineering, Technology, or another relevant field from a recognized university.
The minimum academic requirement is a CGPA of 2.75 out of 4.0 or an internationally equivalent qualification.
The eligibility provision allows graduates from relevant academic disciplines to enter the program, subject to the specified undergraduate qualification and academic performance requirements.
The curriculum incorporates ethical considerations into the study of artificial intelligence.
Students examine questions of ethics and fairness through a compulsory non-credit course and consider the consequences of applying artificial intelligence in different social and organizational settings.
Sustainable development also forms part of the program's academic direction. Students undertake projects relevant to one or more Sustainable Development Goals, while application-oriented subjects examine the use of artificial intelligence in sectors such as agriculture and healthcare.
This connects technical development with questions about the purpose, implications, and evaluation of artificial intelligence applications.
The degree provides preparation for research and technical work involving artificial intelligence, machine learning, data analysis, and intelligent computing systems.
Its academic subjects are relevant to work in technology companies and sectors such as banking, finance, telecommunications, healthcare, agriculture, and industrial automation.
The curriculum also supports preparation for further research, including doctoral study, and for technology-related entrepreneurial activity.
Individual opportunities depend on technical competence, thesis specialization, professional experience, and the requirements of employers or higher education institutions.
It is a two-year, research-based master's program covering machine learning, computer vision, natural language processing, deep learning, practical data science, and artificial intelligence research.
The program lasts two years and is organized into four semesters.
Students complete 50 credits, including 15 core-course credits, four group-project credits, three technical-elective credits, and 28 thesis credits.
Applicants need a four-year Bachelor's degree in Science, Engineering, Technology, or another relevant field, with a minimum CGPA of 2.75 out of 4.0 or an international equivalent.
Yes. The degree includes a 28-credit thesis in artificial intelligence or an application-related research area.
Yes. Students select one three-credit elective from the identified technical elective courses according to their thesis-related academic interests.
Yes. Ethics and fairness are included through a compulsory non-credit course and the program's broader learning objectives.
Meta Title: MAS in Artificial Intelligence at MBUST: Courses and Credits
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