BTech in Artificial Intelligence (BTech in AI)

• Four-year applied AI program integrating computer science, mathematics, cognitive science, machine learning, and project-based industry-oriented training.

• Builds strong foundations in programming, algorithms, statistics, cognitive science, and optimization.
• Provides advanced training in AI domains including ML, deep learning, computer vision, data science, NLP, and pattern recognition.
• Emphasizes hands-on projects each semester plus industrial internship.
• Designed to meet national demand for AI-skilled graduates supporting digital transformation, automation, data analysis, and sustainable development.
• Offers flexibility in specialization because it is not tied to engineering council restrictions.
• Intended to produce graduates who can deploy AI solutions for business intelligence, automation, disaster prediction, smart infrastructure, and community development.

Curricular structure (semester-wise):

Semester I
• Introduction to Intelligent Systems – 3
• Introduction to Programming – 3
• Calculus – 3
• Probability and Statistics – 3
• Ethics and Professional Conducts – 3
• Advanced Communication Skills – 3
Total Credits: 18

Semester II
• Object-oriented Programming – 3
• Data Structures and Algorithms – 3
• Discrete Structures – 3
• Linear Algebra – 3
• Introduction to Cognitive Science – 3
• Semester Project I – 2
Total Credits: 17

Semester III
• Database Management Systems – 3
• Design and Analysis of Algorithms – 3
• Artificial Intelligence – 3
• Introduction to Data Science – 3
• Differential Equations – 3
• Semester Project II – 2
Total Credits: 17

Semester IV
• Software Engineering – 3
• Optimization Theory – 3
• Numerical Methods – 3
• Introduction to Machine Learning – 3
• Programming Paradigms for AI – 3
• Semester Project III – 2
Total Credits: 17

Semester V
• Pattern Recognition – 3
• Data Mining – 3
• Computer Networks & Cyber Security – 3
• Elective I (AICL) – 3
• Innovative Entrepreneurship – 3
• Semester Project IV – 2
Total Credits: 17

Semester VI
• Advanced Machine Learning – 3
• Image Processing – 3
• Elective II (AICL) – 3
• Elective III (MSE) – 3
• Seminar – 2
• Semester Project V – 2
Total Credits: 16

Semester VII
• Deep Learning – 3
• Elective IV (AICL) – 3
• Elective V (AICL) – 3
• Elective VI (MSE) – 3
• Industrial Preparation – 1
• Semester Project VI – 2
Total Credits: 15

Semester VIII
• Elective VII (AICL) – 3
• AI and Society – 3
• Industrial Internship – 6
Total Credits: 12

Credit summary:
• AICL (AI Application Center): 18
• AICS (CS Core): 15
• AICC (AI Core): 27
• MSC (Math & Stats Core): 21
• MSE (Math & Stats Electives): 6
• HAMO (Humanities, Arts, Management): 12
• AISP (Semester Projects): 12
• AIIN (Internship): 7
• AISM (Seminar): 2
Total Credits: 129

Features:
• Strong AI-focused curriculum without engineering council restrictions.
• Extensive project-based learning each semester.
• Industrial internship integrated in final year.
• Emphasis on data-driven decision systems and automated ML pipelines.
• Supports Digital Nepal goals and future AI workforce shortages.
• Develops communication, ethics, teamwork, and interdisciplinary capability.

Eligibility:
• Must have passed XI and XII with PCM (Physics, Chemistry, Mathematics) or PMCs (Physics, Mathematics, Computer Science).

Fee:
• Nepali students: NPR 960,000
• SAARC students: 1.5× domestic fee
• International (non-SAARC): 2× domestic fee

Admission process:
• Apply through KU’s School of Engineering admission system.
• Meet minimum PCM/PMCs subject requirements.
• Merit-based selection following university norms.

Job prospects:
• Machine learning expert
• Deep learning analyst
• AI professional
• AI specialist
• Analytics consultant
• Technology consultant
• Data-driven decision systems developer
• Roles across finance, biotech, energy, industry automation, research labs, and IT sectors