Shaping the next generation
through Computer Science in AI & ML
A four-year undergraduate program that blends rigorous computing foundations with deep specialization in artificial intelligence — built for Cambodia's digital economy and the global stage.
Program
Description
The Bachelor of Science in Computer Science with a specialization in Artificial Intelligence and Machine Learning, offered by the Faculty of Digital Economy at the National University of Management, is a four-year, 120-credit undergraduate program designed to develop technically skilled and ethically grounded computing professionals.
The program blends rigorous foundations in mathematics, programming, and systems design with deep specialization in intelligent systems, data science, and machine learning. Students progress from core computing principles through applied AI development, culminating in a professional internship and a capstone thesis or AI systems project.
The curriculum is structured to reflect the demands of Cambodia's growing digital economy while preparing graduates for global careers and postgraduate study in computing, data science, and artificial intelligence. It is built on eight semesters of progressively advanced coursework spanning discrete mathematics, algorithms, software engineering, machine learning, data engineering, natural language processing, reinforcement learning, and AI ethics.
Program
Aim
"To equip students with the theoretical knowledge, practical skills, and professional competencies required to design, develop, deploy, and govern intelligent computing systems — producing graduates who can lead innovation at the intersection of artificial intelligence, software engineering, and data science, contributing meaningfully to industry, academia, and society."
Program Learning Outcomes
Apply mathematical reasoning, discrete structures, probability, and algorithmic theory to model, analyze, and solve complex computing problems with rigor and precision.
Design, implement, test, and evaluate software applications and computing systems using structured engineering methodologies, modern programming paradigms, and best practices in systems architecture.
Explain and apply core principles of artificial intelligence, including search, knowledge representation, reasoning, and introductory machine learning, to build intelligent computational solutions.
Design, train, evaluate, and optimize machine learning and deep learning models — including CNNs, RNNs, transformers, reinforcement learning agents, and NLP pipelines for complex real-world tasks.
Develop production-ready AI systems by applying MLOps practices, model monitoring, deployment pipelines, and responsible scaling strategies across cloud and edge environments.
Collect, engineer, analyze, and visualize large datasets using statistical methods, data pipelines, and operations research techniques to generate actionable insights and support decision-making.
Demonstrate professional communication, apply AI ethics and governance frameworks, exhibit technopreneurial thinking, and synthesize program competencies through a capstone thesis or AI systems project that addresses a meaningful real-world challenge.