The B.Sc. Information Technology (Artificial Intelligence and Machine Learning) programme offered by the School of Science and Computer Studies (SSCS) is designed to provide students with a strong foundation in information technology and advanced knowledge of artificial intelligence and machine learning. The curriculum combines theoretical learning with practical experience through laboratory sessions, industry projects, internships, and research activities. Students develop technical, analytical, and problem-solving skills to design intelligent solutions for real-world challenges. The programme also emphasises ethical computing, innovation, communication, teamwork, and lifelong learning, enabling graduates to pursue successful careers, higher education, research and entrepreneurship in the rapidly evolving field of AI and digital technology.
3 years / 6 Semesters
Rs.50,000/- per semester
For detailed information on fee concessions and scholarships, please feel free to contact our Admissions Office at +91 9342900666.Regular – Full Time
Candidates should have passed in PUC /12th Grade or Equivalent examination in any stream from a recognised board.
The B.Sc. Information Technology with Artificial Intelligence and Machine Learning programme offered by the School of Science and Computer Studies (SSCS) prepares students for emerging careers in intelligent computing, data-driven technologies, and digital transformation. The programme aims to develop strong technical knowledge in information technology, artificial intelligence, machine learning, data science, cloud computing, and cybersecurity. It emphasises analytical thinking, programming proficiency, innovation, ethical practices, and problem-solving through practical learning, industry collaborations, and research activities. Graduates will be equipped to pursue careers in software development, AI applications, data analytics, higher education, research, and entrepreneurship while adapting to the evolving demands of the global technology industry.
The School of Science and Computer Studies (SSCS), CMR University, follows the Choice-Based Credit System (CBCS) in accordance with the University regulations. This flexible academic framework enables students to select courses based on their interests and programme requirements while earning credits through successful completion of each course.
Choice-Based Credit System (CBCS)
The CBCS framework provides flexibility in learning by allowing students to choose elective and interdisciplinary courses. Credits are awarded based on successful completion of the prescribed curriculum, encouraging academic excellence and multidisciplinary learning.
Grading and Academic Performance
Student performance is evaluated using a letter grading system. At the end of each semester, the Semester Grade Point Average (SGPA) is calculated to assess semester-wise academic achievement, while the Cumulative Grade Point Average (CGPA) reflects the student’s overall academic performance across all semesters.
Curriculum Requirements
Each programme follows an approved curriculum that includes core courses, elective courses, laboratory sessions, internships, projects, and other academic requirements. To promote self-paced and lifelong learning, students are encouraged to complete relevant SWAYAM and Massive Open Online Courses (MOOCs) offered by recognised institutions, subject to University guidelines.
Audit Courses
Students may enrol in audit courses to acquire additional knowledge and develop new skills without earning academic credits or grades. These courses provide opportunities to enhance professional competencies and improve career readiness.
Evaluation System
The University adopts a Continuous and Comprehensive Evaluation (CCE) approach. Assessment consists of Continuous Internal Evaluation (CIE) and the Semester End Examination (SEE), each carrying 50% weightage, resulting in a total of 100 marks for every course, irrespective of the credit assigned.
Continuous Internal Evaluation
The Continuous Internal Evaluation is conducted throughout the semester using a variety of assessment methods, including assignments, quizzes, seminars, presentations, laboratory work, case studies, group discussions, practical exercises, project reviews, and industry-based activities. The evaluation pattern and weightage are determined by the course instructor in accordance with University regulations.
Semester End Examination
The Semester End Examination is conducted at the end of each semester for all eligible students enrolled in the respective courses. Certain courses that are entirely practice-oriented or based on continuous assessment may be evaluated solely through CIE, as specified in the curriculum.
Makeup Examinations
Students who are unable to pass one or more courses in the Semester End Examination are provided with an opportunity to appear for makeup examinations in accordance with the regulations of CMR University. Successful completion of these examinations enables students to improve their academic performance and progress toward the award of the degree.
Upon successful completion of the B.Sc. Information Technology with Artificial Intelligence and Machine Learning programme offered by the School of Science and Computer Studies (SSCS), CMR University, graduates will be able to:
PO1: Computing Knowledge
Apply fundamental concepts of Information Technology, Artificial Intelligence, Machine Learning, mathematics, and computing principles to identify, analyse, and solve real-world computing problems.
PO2: Problem Analysis
Analyse complex technical problems using logical reasoning, data-driven approaches, and computational techniques to develop efficient, reliable, and scalable solutions.
PO3: Design and Development of Intelligent Solutions
Design, develop, implement, and evaluate software applications and AI-based systems using appropriate programming languages, algorithms, modern tools, and emerging technologies to address societal and industrial needs.
PO4: Research and Innovation
Apply research methodologies, analytical skills, and experimental techniques to investigate computing challenges, interpret results, and contribute innovative solutions in Information Technology and Artificial Intelligence.
PO5: Modern Tool Usage
Select and effectively use contemporary software tools, cloud platforms, AI frameworks, data analytics techniques, and development environments for building secure and intelligent computing applications.
PO6: Professional Ethics and Sustainability
Demonstrate ethical responsibility, professional integrity, cybersecurity awareness, and an understanding of the social, environmental, legal, and sustainability aspects associated with Information Technology and Artificial Intelligence.
PO7: Communication and Teamwork
Communicate technical concepts effectively through oral, written, and digital media, and collaborate productively as an individual or as a member of multidisciplinary teams.
PO8: Lifelong Learning and Employability
Recognise the importance of continuous learning, professional development, entrepreneurship, and adaptability to emerging technologies, enabling successful careers, higher education, research, and leadership in the global IT industry.
PO9: Leadership and Project Management
Demonstrate leadership, project planning, decision-making, and management skills to successfully execute Information Technology and Artificial Intelligence projects while working effectively in multidisciplinary and multicultural environments.
PO10: Industry Readiness and Entrepreneurial Mindset
Develop industry-relevant technical competencies, innovative thinking, and entrepreneurial skills to create technology-driven solutions, adapt to evolving industry requirements, and contribute to startups, organisations, and society.
Upon successful completion of the B.Sc. Information Technology with Artificial Intelligence and Machine Learning programme, students will be able to:
CO1: Apply the fundamental concepts of Information Technology, Artificial Intelligence, Machine Learning, mathematics, and programming to solve computing problems effectively.
CO2: Design, develop, test, and deploy software applications and intelligent systems using appropriate programming languages, algorithms, databases, and modern computing tools.
CO3: Analyse structured and unstructured data using statistical methods, machine learning techniques, and data visualisation tools to support informed decision-making.
CO4: Demonstrate professional ethics, cybersecurity awareness, effective communication, teamwork, and project management skills while developing technology-driven solutions for real-world applications.
CO5: Engage in lifelong learning by adapting to emerging technologies, conducting research, pursuing innovation, and applying entrepreneurial skills to address industrial and societal challenges.
The B.Sc. Information Technology with Artificial Intelligence and Machine Learning programme offered by the School of Science and Computer Studies (SSCS), CMR University is designed to bridge academic learning with industry expectations. Students gain the following academic and professional competencies:
Graduates of the B.Sc. Information Technology with Artificial Intelligence and Machine Learning programme offered by the School of Science and Computer Studies (SSCS), CMR University can pursue diverse career opportunities in IT and technology-enabled industries. Some of the prominent career roles include:
These career opportunities enable graduates to work across sectors such as Information Technology, Banking and Financial Services (BFSI), Healthcare, Manufacturing, Retail, E-commerce, Telecommunications, Education, Government, Logistics, and emerging technology-driven enterprises. The industry-oriented curriculum, internships, capstone projects, and placement support at the School of Science and Computer Studies (SSCS), CMR University prepare students to excel in these professional roles.
Placement Support, Industry Exposure, Internships, and Capstone Projects
The School of Science and Computer Studies (SSCS), CMR University is committed to enhancing the employability of graduates through a dedicated Training and Placement Cell, strong industry partnerships, and experiential learning opportunities. The department works closely with industry leaders to ensure that students are equipped with the technical, professional, and interpersonal skills required to succeed in today’s competitive technology landscape.
The Placement Team at SSCS plays a vital role in preparing students for successful careers by:
To bridge the gap between academia and industry, SSCS regularly conducts:
Industrial Experience through Internships
The curriculum emphasises mandatory internships to provide students with practical exposure to industry practices. During internships, students:
Internships enable students to build confidence, strengthen technical competencies, and improve their prospects for full-time employment.
The programme culminates in a Capstone Project, enabling students to integrate the knowledge and skills acquired throughout the programme. Students work individually or in teams to identify, design, develop, implement, and evaluate innovative technology solutions for real-world problems.
Capstone projects are often undertaken in collaboration with industry partners, research organisations, or faculty mentors and encourage students to:
Through its comprehensive placement initiatives, industry engagement, internships, and capstone projects, the School of Science and Computer Studies (SSCS), CMR University ensures that graduates are industry-ready, professionally competent, and well prepared for successful careers in the global Information Technology and Artificial Intelligence ecosystem.
Eligibility Criteria
Undergraduate Programmes (BCA General, BCA in Data Science, BCA in Cloud Computing, BCA in Game Development, and B.Sc. IT in AI and ML)
Application Process
Upload the necessary documents, including mark sheets, identity proof, and passport-size photographs.
Pay the application fee online.
Selection Process
Confirmation of Admission
Orientation & Enrollment
67,500/- Per semester - OMBR
Undergraduate 
3 Years 
OMBR & Lakeside 
67,500/- OMBR Per semester Undergraduate 3 years OMBR & Lakeside 
67,500/- Per semester Undergraduate 3 years OMBR 
67,500/- Per Semester Undergraduate 3 years OMBR 67,500/- Per semester - OMBR Undergraduate 3 Years Lakeside & OMBR Campus 1,00,000 Per semester -OMBR Postgraduate 2 Years OMBR & Lakeside Campus 45000 Per semester Postgraduate 2 Years OMBR 1,00,000 Per semester -OMBR Postgraduate 2 Years OMBR & Lakeside Campus 
