The course gives an understanding of how inputs such as language, vision, and huge amounts of data can be used for decision-making. You will begin with courses in computer programming, mathematics, and an introduction to the field of robotics and artificial intelligence along with topics on neural networks and computer vision, machine learning, and natural language processing.
Salient Features
- International standard L-T-W (Lecture, Tutorial, and Workshop) approach in teaching that provides students with practical experience beyond the classroom.
- The updated curriculum to equip students with the ability to transfer their knowledge to use.
- Exposure to industry's working through experiential learning as well as workshops and lecture sessions by global and local experts in the sector.
- Exposure to the international industry through field-based visits.
- Modern and practical education-based college dedicated to quality academic performance and student experience.
- Long Experience in providing quality and industry-ready IT and Business.
- Partnership with established international universities and institutions from UK and Singapore.
- Contribution to society locally, nationally, and internationally.
- Learning experience beyond textbooks and classrooms
Eligibility
Academic Level
NEB +2 overall aggregate of 2.2 CGPA (55%) or above with each subject (theory and practical) grade D+ or above, and SEE Mathematics score of C+ (50%) or above
For A-Levels, a minimum of 3.5 credits and at least a grade of D and above
English Proficiency
English NEB XII Marks > 60% or 2.4 GPA
Applicants not meeting the aforementioned criteria for English can demonstrate their English proficiency with the following internationally recognized English Tests:
For Level 4 or Year 1 BIT
Pass in General Paper or English Language or IELTS 6 or PTE 53
Students not meeting the aforementioned criteria will have to pursue University Foundation Course (UFC). UFC is a one-year academic program of London Metropolitan University (LMU) designed to fulfill undergraduate admission requirements.
Curricular Structure
Year 1
| Programming | Credits: 30 |
| Introduction to Information Systems | Credits: 15 |
| Fundamentals of Computing | Credits: 15 |
| Calculus and Linear Algebra | Credits: 30 |
| Introduction to Robotics and IoT | Credits: 30 |
Year 2
| Data Structure and Specialist Programming | Credits: 30 |
| Software Engineering | Credits: 30 |
| Database | Credits: 15 |
| Probability and Statistics | Credits: 15 |
| Further Calculus | Credits: 15 |
| Applied Data Science | Credits: 15 |
Year 3
| Project | Credits: 30 |
| Big Data and Data Mining | Credits: 30 |
| Work related Learning II | Credits: 15 |
| Artificial Intelligence | Credits: 15 |
| Computer Vision | Credits: 15 |
| Applied Machine Learning | Credits: 15 |