Programme Overview

The Bachelor of Engineering in Artificial Intelligence and Machine Learning at RNTU is designed to equip students with the knowledge and practical skills required to excel in the rapidly evolving world of intelligent technologies. The programme combines core computer science with advanced AI and ML concepts, focusing on real-world applications in areas like healthcare, finance, agriculture, and smart systems. Supported by a team of experienced researchers and data scientists, the department emphasizes hands-on learning through projects, internships, and industry exposure. Students work with tools like Python, TensorFlow, and cloud platforms to develop innovative solutions to complex problems. The department also encourages ethical thinking and interdisciplinary collaboration, offering training programmes and expert sessions to keep students aligned with global technological trends. Through strong academic and industry partnerships, the programme prepares graduates for impactful careers and future-ready innovation where intelligent systems work alongside humans to build a better world.

  • 4 Years

    Duration of programme

  • UG

    Level of Study

Key Highlights

Well-Equipped Central Artificial Intelligence Laboratory
Wi-Fi Enabled Smart Campus
Dedicated IoT and Advanced Research Lab
Technology-Integrated Smart Classrooms
Highly Qualified and Experienced Faculty Members

How will you benefit

Diverse Career Opportunities: A B.E. in Artificial Intelligence and Machine Learning opens doors to a wide range of job roles across sectors such as software development, IT consulting, telecommunications, healthcare, finance, and e-commerce.
Attractive Salary Packages: Graduates with AI and ML expertise are in high demand, and often receive competitive salary offers due to their specialized and industry-relevant skills.
Innovation and Problem-Solving: The programme promotes innovation, creativity, and analytical thinking. Students are encouraged to design intelligent, real-world solutions through hands-on projects and research-driven learning.
Excellent Career Growth: Graduates can progress into specialized fields such as Artificial Intelligence, Machine Learning, Data Science, Cybersecurity, and Software Engineering, with significant opportunities for leadership and advancement.

What will you study

Programming Languages: You will learn programming languages such as Python, R, and MATLAB, which are widely used in Artificial Intelligence and Machine Learning for data analysis, model development, and automation.

Optimization Methods: You will study optimization techniques such as gradient descent and convex optimization, which are used to find the best possible solutions in training machine learning models.

Linear Algebra: Linear algebra, which deals with vectors, matrices, and linear transformations, forms the mathematical foundation of many machine learning algorithms and deep learning models.

Probability and Statistics: You will explore fundamental concepts such as probability theory, hypothesis testing, and regression analysis, which are essential for making data-driven decisions and understanding model behavior.

  • PO-1 – Engineering Knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and computer engineering principles to solve complex problems in artificial intelligence and machine learning.
  • PO-2 – Problem Analysis: Identify, formulate, review research literature, and analyze complex engineering problems using principles of mathematics, natural sciences, and engineering sciences.
  • PO-3 – Design/Development of Solutions: Design solutions for complex engineering problems and design system components or processes that meet specified needs with appropriate consideration for public health, safety, cultural, societal, and environmental concerns.
  • PO-4 – Conduct Investigations of Complex Problems: Use research-based knowledge and methods, including design of experiments, analysis and interpretation of data, and synthesis of information to provide valid conclusions.
  • PO-5 – Modern Tool Usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools, including prediction and modeling, to complex engineering activities, with an understanding of their limitations.
  • PO-6 – The Engineer and Society: Apply reasoning informed by contextual knowledge to assess societal, health, safety, legal, and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
  • PO-7 – Environment and Sustainability: Understand the impact of professional engineering solutions in societal and environmental contexts and demonstrate the knowledge of, and need for, sustainable development.
  • PO-8 – Ethics: Apply ethical principles and commit to professional ethics, responsibilities, and norms of engineering practice.
  • PO-9 – Individual and Team Work: Function effectively as an individual, and as a member or leader in diverse teams and in multidisciplinary settings.
  • PO-10 – Communication: Communicate effectively on complex engineering activities with the engineering community and with society at large, such as being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.
  • PO-11 – Project Management and Finance: Demonstrate knowledge and understanding of engineering and management principles and apply these to one’s own work, as a member or leader in a team, to manage projects and in multidisciplinary environments.
  • PO-12 – Life-long Learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.

  • PSO-1- Graduates will demonstrate proficiency in designing, implementing, and optimizing advanced algorithms and models for artificial intelligence and machine learning applications, including neural networks, deep learning, and reinforcement learning.
  • PSO-2- Graduates will be capable of developing intelligent systems that can perceive, reason, and act autonomously or semi-autonomously, applying AI techniques to solve complex problems across various domains such as healthcare, finance, education, and smart systems.
  • PSO-3- Graduates will apply ethical principles in the development and deployment of AI systems, ensuring fairness, transparency, accountability, and privacy, in alignment with societal values and legal standards.

  • PEO-1- Graduates will demonstrate strong proficiency in artificial intelligence, machine learning algorithms, and software development techniques relevant to intelligent systems.
  • PEO-2- Graduates will effectively apply AI and ML methodologies to solve real-world problems across diverse domains such as healthcare, finance, education, and smart technologies.
  • PEO-3- Graduates will uphold ethical standards and consider the societal, legal, and environmental impacts while developing and deploying AI-based solutions.
  • PEO-4- Graduates will pursue lifelong learning and stay updated with emerging technologies and industry trends through higher education, certifications, or professional development.
  • PEO-5- Graduates will exhibit leadership, communication, and collaboration skills, functioning effectively in multidisciplinary and multicultural teams.

Curriculum

  • Engineering Chemistry, Mathematics-I, Engineering Graphics, Basic Electrical Engineering, Basic Computer Engineering, Manufacturing Practices, Entrepreneurship development

  • Engineering Physics, Mathematics-II, Basic Mechanical Engineering, Basic Civil & Engg Mechanics, Communication Skills, Health, Hygiene & Yoga, Rural Outreach

  • Mathematics-III, Discrete Structures, Programming in Java, Object Oriented programming & Methodology, Data structures, Computer Programming & Tools-I(HTML), Skill Electives-I

  • Mathematics & Statistics for Machine Learning, Advance Computer Architecture, Analysis and Design of Algorithm, Python Programming, Operating System, Computer Programming and Tools, Skill Electives-II

  • Introduction to AI, Machine Learning, Data Base Management Systems, Software Engineering and Agile Methodologies, Web Programming Fundamentals, Unix and Shell Programming Lab, Skill Elective-III

  • Natural Language Processing, Deep Learning Fundamentals, High End Programming (Elective), Computer Networking, Computer Vision & Image Processing, Report Writing using Latex, Skill Electives -IV

  • Robotics, Fundamentals of Cybersecurity, Cloud and Distributed Computing, Information Storage & Management, Professional Elective-I

  • Soft Computing, Reinforcement Learning, Professional Elective-II, Open Elective-II, Professional Elective-I

CAREERS AND EMPLOYABILITY

Robotics Engineer
Business Intelligence Analyst with Focus on AL
Data Scientist
AI Business Analyst

ELIGIBILITY CRITERIA

12 th in PCM with 45 %(40% in case of SC/ST)

3 year diploma in any stream

 

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Rabindranath Tagore University,

Rabindranath Tagore University, Mendua, Post: Bhojpur, Near - Bangrasiya chouraha, Bhopal-Chiklod Road, District: Raisen,(M.P.)

07552700401 | 07552700447 |

info@rntu.ac.in

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