BTech in Artificial Intelligence (BTech in AI)
Pre-registration
The B. Tech in AI at Global College of Social Science and Technology (GCST), affiliated with Purbanchal University, is one of Nepal’s most sought-after IT programs. Spanning over four years across eight semesters, the course combines theoretical depth with strong practical engagement, preparing students for impactful careers in AI, machine learning, and emerging technologies.
This program is more than a degree-it’s a transformative journey that builds leadership, innovation, and real-world problem-solving skills. Designed to help students thrive in the global digital economy, B. Tech in AI at GCST shapes tech-savvy, socially responsible professionals ready to lead change.
Vision
GCST’s B. Tech in AI program is envisioned to be a national and global model for excellence in AI education, producing ethical and visionary leaders for a tech-driven world.
Mission
- Provide practical and ethical education in AI.
- Promote critical thinking, innovation, and interdisciplinary learning
- Bridge academia with the industry and research sectors
- Cultivate responsible leadership for equitable tech advancement
Objectives
- Deliver solid theoretical and hands-on knowledge in AI
- Offer real-world learning via projects and industry links
- Enhance decision-making and problem-solving skills
- Encourage ethical and innovative AI development
- Support entrepreneurship and global collaboration
- Promote research in AI, robotics, and automation
- Prepare graduates for success in diverse global roles
Special Benchmarks of B.Tech in AI at GCST
- Industry-responsive and globally recognised benchmarked curriculum
- Cutting-edge technology-enabled teaching and AI simulation tools
- Strong focus on experiential learning through capstone projects and industry internships
- A dynamic faculty team with academic depth and industry insights
- Exposure to tech firms through institutional partnerships for research, mentoring, and placement
Key Area of Focus
- Machine Learning
- Natural Language Processing
- Robotics & Intelligent Systems
- Business Intelligence and Automation
- Ethical AI & Responsible Innovation
- Big Data & Cloud-based AI Applications
- Deep Learning & Neural Networks
Course Structure
| Year | Semester | Course Title | Credits |
| Year I | Semester I | Total Credits: 18 | |
| Introduction to Programming | 3 | ||
| Digital Logic | 3 | ||
| Calculus | 3 | ||
| Introduction to Artificial Intelligence | 3 | ||
| Technical Report Writing and Presentation | 3 | ||
| Society and Professional Ethics | 3 | ||
| Semester II | Total Credits: 17 | ||
| Object Oriented Programming | 3 | ||
| Artificial Intelligence and Intelligent Systems | 3 | ||
| Microprocessor and Assembly Language | 3 | ||
| Probability and Statistics | 3 | ||
| Linear Algebra | 3 | ||
| Project-I | 2 | ||
| Year II | Semester I | Total Credits: 17 | |
| Database Management System | 3 | ||
| Data Structure and Algorithm | 3 | ||
| Computer Organization | 3 | ||
| Operating System | 3 | ||
| Differential Equations | 3 | ||
| Project-II | 2 | ||
| Semester II | Total Credits: 17 | ||
| Numerical Methods | 3 | ||
| Computer Networks | 3 | ||
| Design and Analysis of Algorithms | 3 | ||
| Programming for AI | 3 | ||
| Introduction to Data Science | 3 | ||
| Project-III | 2 | ||
| Year III | Semester I | Total Credits: 17 | |
| Machine Learning | 3 | ||
| Optimization Techniques | 3 | ||
| Embedded Systems | 3 | ||
| Pattern Recognition and Image Processing | 3 | ||
| Data Warehousing and Mining | 3 | ||
| Project-IV | 2 | ||
| Semester II | Total Credits: 17 | ||
| Object Oriented Software Engineering | 3 | ||
| Computer Vision | 3 | ||
| Deep Learning | 3 | ||
| Speech and Natural Language Processing | 3 | ||
| Research Methods | 3 | ||
| Project-V | 2 | ||
| Year IV | Semester I | Total Credits: 11 | |
| Internship | 3 | ||
| Reinforcement Learning | 3 | ||
| Cloud Computing | 3 | ||
| Project-VI | 2 | ||
| Semester II | Total Credits: 12 | ||
| IoT for Smart City | 3 | ||
| Elective-I | 3 | ||
| Elective-II | 3 | ||
| Professional Project | 3 |
List of Elective Courses
| Specialization | Course Title |
| Computer Vision | Image and Video Processing |
| Surveillance Video Analytics | |
| People Detection and Bio-metric Recognition | |
| Mathematics | Introduction to Statistical Learning |
| Optimization Methods in Machine Learning/ Convex Optimization | |
| Kernel Methods | |
| Bayesian Data Analysis | |
| Computing/IoT | Smart Product Development |
| Predictive Analysis and IoT | |
| Modeling & Simulation | |
| Pervasive Computing | |
| Data Science | Fundamentals of Big Data Analytics |
| Data Analysis and Visualization | |
| Business Intelligence | |
| Social Media Analytics |
Eligibility for Admission
- Should have completed twelve years of schooling with Mathematics.
- Should have scored a minimum D+ grade in each subject of 10+2 with a CGPA of 2.0 or above
