Candidates should have passed the 2nd PUC / 12th / Equivalent examination with a minimum of 45% marks (40% for Karnataka reserved category candidates) in Physics and Mathematics along with Chemistry / Computer Science / Electronics / Information Technology / Biology / Informatics Practices / Biotechnology / Technical Vocational Subject / Agriculture / Engineering Graphics / Business Studies / Entrepreneurship from a recognised board. Candidates must also qualify in one of the following entrance exams: CET / COMED-K / JEE / Other state entrance tests / National-level entrance tests OR Passed D.Voc stream in the same or allied sector.
Scope & Objectives
Artificial Intelligence and Data Science (AI & DS) is an interdisciplinary branch of computer science that combines advanced computing, statistical methods, and data analysis to enable intelligent decision-making and predictive modelling. This field focuses on designing systems that can analyse large datasets, uncover patterns, and make data-driven decisions, transforming industries ranging from healthcare and finance to e-commerce and autonomous systems.
According to the National Association of Software and Services Companies (NASSCOM), the AI and Data Science sector is projected to reach $17 billion by 2030, highlighting the growing need for skilled professionals in these domains.
To address this demand and equip students with cutting-edge expertise, CMR University offers a comprehensive and specialised B.Tech programme in Artificial Intelligence and Data Science.
This pioneering B.Tech in Computer Science and Engineering with specialisation in AI & DS is designed to provide a strong theoretical foundation in AI, machine learning, and data science, along with hands-on experience in real-world applications. Graduates will be well-prepared to tackle complex problems, drive data-driven innovation, and lead the digital transformation across sectors.
Programme Structure
SEMESTER I (Physics)
4MATH1012 – Engineering Mathematics with Python Lab-I
4ENME2141 – Advanced Robotics: Integration of Control, Programming, and AI, 4ENCV2111 – Geographic Information System & Applications, 4ENCC1021 – Advanced Digital Image Processing
SEMESTER VII
4CSPL2041 – Machine Learning
4CSGC3XX1 – Professional Elective 3
4CSGC3XX1 – Professional Elective 4
CPSHS1021 – Business Management for Engineers
IDE Elective 3 – Interdisciplinary Elective 3
4CAPS4010 – Capstone Project Phase I
4INTS4010 – Internship-III
Professional Elective 3 & 4 Examples (AI):
4CSPL4021 – Deep Learning, 4CSPL4031 – Big Data & Analytics, 4CSPL4041 – Robotic Process Automation, 4CSPL4051 – Natural Language Processing
Professional Elective 3 & 4 Examples (Cybersecurity):
4CSPL4061 – Mobile Computing Security, 4CSPL4071 – Digital Forensics, 4CSPL4081 – Cloud Computing Security (MOOC), 4CSPL4091 – Web Security
Professional Elective 3 & 4 Examples (Application Development):
Choice-Based Credit System (CBCS): The university follows CBCS, allowing students to choose courses and earn credits based on their performance.
Grades and GPA: Students are awarded grades for each course per semester, and their Semester Grade Point Average (SGPA) is calculated. The Cumulative Grade Point Average (CGPA) reflects overall academic performance.
Prescribed Curriculum: The curriculum includes required courses, labs, and other degree requirements. It also incorporates SWAYAM and MOOC platforms.
Auditing Courses: Students can audit courses to gain knowledge without grade pressure—an advantage during placements.
Evaluation System: Continuous Internal Evaluation (CIE) and Semester End Examination (SEE) are conducted, each contributing 50% to the final marks.
Assessment Methods: Faculty may select methods such as assignments, seminars, group discussions, presentations, industry reports, etc.
Makeup Examinations: Students who fail the SEE are eligible to retake exams to improve their grades.
Programme Outcome
Engineering Knowledge: Apply mathematical, statistical, and computational knowledge to analyse and solve complex problems in AI, machine learning, and data science.
Design/Development of Solutions: Design intelligent systems, data-driven models, and AI applications considering societal, ethical, and environmental concerns.
Investigation of Complex Problems: Conduct research, data analysis, and experimentation to extract insights and make evidence-based decisions in AI and data science contexts.
Modern Tool Usage: Utilise AI frameworks, data analytics tools, programming languages, and modern IT platforms for complex engineering and data science tasks.
Engineer and Society: Evaluate the social, legal, and cultural implications of AI & data-driven technologies on society.
Environment and Sustainability: Understand the environmental impact of AI solutions and promote sustainable and responsible AI practices.
Ethics: Uphold professional ethics and responsibility in the design, deployment, and use of AI and data-driven systems.
Individual and Teamwork: Collaborate effectively, both individually and as part of multidisciplinary teams, to develop AI and data science solutions.
Communication: Communicate technical concepts, data insights, and AI-driven solutions clearly to peers, professionals, and society at large.
Project Management and Finance: Apply principles of project management, budgeting, and resource planning to AI and data-driven projects.
Life-long Learning: Engage in continuous learning to stay updated with emerging AI, machine learning, and data science technologies.
What Expertise Do You Gain?
Artificial Intelligence & Machine Learning Concepts – Understand AI fundamentals, supervised & unsupervised learning, deep learning, and neural networks.
Programming & Computational Skills – Gain proficiency in Python, R, SQL, and other languages used for AI & data science.
Data Analytics & Statistical Modelling – Apply statistical techniques, probability, and data analysis to extract actionable insights.
Machine Learning Algorithms & Frameworks – Work with TensorFlow, PyTorch, Scikit-learn, and other ML tools for real-world problem-solving.
Data-Driven Application Development – Build AI-powered applications, dashboards, predictive models, and intelligent systems.
Career Opportunities
01
AI Engineer / ML Engineer
02
Deep Learning Specialist
03
Natural Language Processing (NLP) Engineer
04
Computer Vision Engineer
05
Data Scientist
06
Data Analyst
07
Business Intelligence (BI) Analyst
08
Big Data Engineer
09
Robotics Engineer
10
AI Product Manager
11
AI Consultant
12
Ethical AI Specialist
How to apply?
Step 1: Application Enquiry Receive on-call guidance and step-by-step instructions for completing the application form.
Step 2: Selection Process Shortlisting of candidates after document verification, interview, and CMRUAT entrance exam.
Step 3: Offer Letter Provision of the admission offer letter to initiate the enrolment process.
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Rs.1,75,000/- Per semester 
Undergraduate 
4 years 
Lakeside Campus 
