The Bachelor of Science in Artificial Intelligence (BS in AI) at Presidential Graduate School in New Baneshwor, Kathmandu, is a four-year undergraduate program carrying 120 credit hours across 40 courses.
The program focuses on the computing, programming, data, and mathematical knowledge used to develop intelligent systems. Its academic coverage includes machine learning, data science, Python, deep learning, natural language processing, computer vision, responsible AI, and applied project work.
The degree is connected with Westcliff University, USA. Presidential Graduate School serves as the Kathmandu teaching campus, with academic and technical facilities supporting technology students through computer labs, project spaces, research resources, an Innovation Lab, and an IoT & Electronics Lab.
| Particular | Details |
|---|---|
| Course | Bachelor of Science in Artificial Intelligence |
| Short Form | BS in AI |
| Level | Bachelor's Degree |
| School | School of Technology |
| College | Presidential Graduate School |
| Affiliation | Westcliff University, USA |
| Location | Thapagaun, New Baneshwor, Kathmandu, Nepal |
| Duration | 4 Years |
| Total Credits | 120 Credit Hours |
| Total Courses | 40 Courses |
| Main Study Areas | AI, machine learning, data science, NLP, computer vision, Python and deep learning |
The BSc in Artificial Intelligence is built around the technologies used to create systems capable of learning from data, recognizing patterns, processing information, and making computational decisions.
Artificial intelligence study at bachelor's level requires more than learning how to use existing software. Students need programming ability, mathematical reasoning, data skills, an understanding of algorithms, and knowledge of how computing systems are designed. The PGS program brings these areas together with specialized AI study.
Machine learning forms a major part of this academic direction. Students work with the principles used to train computational models from data and examine how such models can be applied to prediction, classification, pattern recognition, and other tasks.
Data science and data analytics provide another part of the degree. AI systems depend heavily on data, so students need to understand how information is collected, organized, examined, and prepared for computational use.
The curriculum also moves into specialized areas such as natural language processing and computer vision. These subjects deal with two important areas of AI: working with human language and interpreting visual information.
The 120-credit curriculum combines computing foundations with specialized artificial intelligence subjects and applied work. Rather than concentrating on a single AI technique, the degree covers several connected areas.
Programming is central to AI study. Python is included within the academic coverage of the program and provides a programming base for work involving data, algorithms, and machine learning.
Students also encounter broader computing concepts needed to understand how intelligent applications operate. These foundations are important because AI models ultimately have to work within software and computing systems.
Data is a core component of artificial intelligence. The program includes data science and analytics-related study that supports the process of working with structured and unstructured information.
Students may work with tasks such as:
These abilities form part of the technical preparation needed for later AI subjects.
Machine learning examines methods through which computer systems can identify relationships in data and use those relationships for computational tasks.
Study in this area introduces students to the logic behind machine-learning models rather than treating AI only as an end-user technology. It connects programming, data, mathematics, and algorithmic reasoning.
This part of the program provides the academic base for more specialized work in areas such as predictive modeling, language processing, computer vision, and deep learning.
Deep learning extends machine-learning study into computational models designed to handle more complex patterns and larger amounts of data.
Its inclusion gives students exposure to a branch of AI widely associated with language, image, and other data-intensive applications.
Deep learning is studied as part of the wider AI curriculum, alongside the programming, analytical, and mathematical knowledge required to understand how such systems operate.
Natural language processing, commonly known as NLP, deals with computational methods for working with human language.
This area can involve the analysis and processing of text and other language-related information. Students studying NLP encounter the connection between language data, machine learning, algorithms, and intelligent applications.
NLP also gives students a specialized area in which broader programming and AI knowledge can be applied to language-related problems.
Computer vision focuses on the computational interpretation of visual information.
The subject connects AI with image-based data and gives students exposure to methods used when computers need to identify, classify, or interpret visual content.
Together, computer vision and NLP introduce students to two distinct types of AI application: visual information and human language.
Technical ability is only one part of studying artificial intelligence. The curriculum also includes responsible AI, ethics, and governance-related themes.
These subjects address questions surrounding how intelligent systems are designed, used, and evaluated. They give students space to consider the consequences of automated decisions and the responsibilities involved in developing or applying AI systems.
This becomes particularly relevant when AI applications involve people, personal information, automated decisions, or wider organizational use.
Artificial intelligence requires practical work alongside conceptual study. Programming exercises, data analysis, technical assignments, and project work allow students to apply the principles covered in class.
Capstone work forms part of the curriculum. A capstone gives students an opportunity to combine knowledge from different parts of the degree in a substantial academic or technical project.
A student working on an AI project may need to bring together several abilities: defining a problem, working with data, selecting an approach, writing code, testing results, interpreting findings, and presenting the completed work.
This combination is useful because AI projects rarely depend on a single subject. Programming, data, algorithms, mathematics, model development, testing, and communication can all contribute to the final result.
BSc in Artificial Intelligence students study at the Presidential Graduate School campus in Thapagaun, New Baneshwor. Several campus facilities are relevant to technology-based study.
Computer laboratories provide spaces for programming, assignments, research, and other computing work. Students can also use digital academic resources for research and project preparation.
The Innovation Lab provides facilities for project development and prototyping. Its resources include 3D printers, laser cutters, electronics equipment, robotics-related tools, and IoT resources. This can be relevant when an AI project connects software with physical devices or prototype development.
The IoT & Electronics Lab adds resources for work involving sensors, embedded systems, electronics, and automation. These facilities can support projects that combine intelligent software with connected or electronic devices.
Research resources and the Center for Research provide additional support for students working on academic inquiry, project reports, and research-oriented assignments.
The academic areas within the BSc in Artificial Intelligence can help students build abilities connected with:
These skills develop through a combination of academic subjects, programming practice, analytical work, and projects rather than through one standalone part of the degree.
The program may suit students who are interested in the technical side of artificial intelligence rather than only using AI applications.
It is particularly relevant to students who want to spend substantial time working with programming, data, computational methods, and problem solving. Interest in mathematics and logical reasoning can also be useful because machine learning and data-focused study involve quantitative concepts.
Students interested in language technologies, intelligent software, computer vision, automation, robotics, or data-intensive computing may also find areas of the curriculum connected with their interests.
The degree can support preparation for technical roles connected with artificial intelligence, machine learning, data, automation, and intelligent systems.
Career directions associated with the program include:
The degree itself does not guarantee entry into a particular role. Career opportunities can depend on technical ability, projects, work experience, further specialization, employer requirements, and the type of position being pursued.
Graduates may also continue academic study in areas connected with artificial intelligence, machine learning, information technology, computing, data, or related technical disciplines where the relevant entry requirements are met.
Studying AI at Presidential Graduate School also places students within a wider School of Technology that includes programs in information technology, cybersecurity, and software engineering.
This creates overlap in several technical areas. Programming, computing systems, data, networks, software, IoT, and project development can intersect with AI study, particularly when students work on applications rather than isolated algorithms.
Campus facilities support these different forms of work through computer laboratories, networking resources, the IoT & Electronics Lab, Innovation Lab, research facilities, and technical support.
PGS also has career services, a Writing Center, Skill Lab activities, workshops, guest sessions, and student clubs. For AI students, these services sit alongside the technical side of the degree and can support academic communication, project presentation, research, and career preparation.
Duration: The program runs for four years.
Credits: The program carries 120 credit hours.
The BSc in Artificial Intelligence consists of 40 courses across its four-year structure.
The academic coverage includes machine learning, data science, Python, deep learning, natural language processing, computer vision, responsible AI, and capstone work.
Yes. Programming forms part of the technical foundation of the degree, with Python included in the curriculum.
Yes. Machine learning is one of the central academic areas of the BSc in Artificial Intelligence, alongside data science and other specialized AI subjects.
Yes. Both natural language processing and computer vision are included within the program's AI study areas.
Possible career directions include AI Engineer, Machine Learning Engineer, Data Scientist, AI Researcher, Automation Specialist, and Robotics Engineer. Individual career outcomes depend on the requirements of the role and the student's skills and experience.
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