Engineering Intelligence for the Future
From smart assistants to self-driving cars, AI is changing how the world works. At COEB, we look past basic coding. Our program gets you directly into Artificial Intelligence, Machine Learning, and data systems. It is a practical, hands-on degree for students who want to build smart software and solve real-world industry challenges.
- Artificial Intelligence
- Machine Learning
- Data Automation
Career Opportunities
AI & ML Engineer
Write and train live algorithms that learn from data, predict market trends, and automate complex company tasks.
Data Scientist & Analyst
Clean up messy corporate data, find hidden patterns, and build predictive models to help executives make major decisions.
Intelligent App Developer
Design smart consumer products, including recommendation engines, interactive chatbots, virtual assistants, and cloud platforms.
Core Focus Areas
Our curriculum offers diverse tracks to master cutting-edge technologies.
Deep Learning & Neural Networks
Train AI models to understand human speech, recognize images, and make quick, automated decisions on the fly.
Data Engineering & Cloud Systems
Go beyond basic databases. Learn to build and scale secure cloud setups that handle massive data streams without crashing.
Robotics & Industry 4.0
Put smart software inside physical hardware. Program smart devices, automate factory workflows, and control autonomous machinery.
HOD's Message
"We strive to cultivate technical excellence and ethical leadership in our students, preparing them for the challenges of tomorrow."
Dr. Tanmaya Kumar Das
Associate Professor & HOD, Computer Science & Engineering(AI &ML)Faculty Profile
Dr. Tanmaya Kumar Das
Associate Professor & HOD
Dr. Sonali Pradhan
Associate Professor
Mr. Ambuja Kumar Parida
Assistant Professor
Ms. Krishna Priyadarsani
Assistant Professor
Ms. Nibedita Shial
Assistant Professor
Ms. Swatismita Das
Assistant Professor
Laboratories
Programming and AI Fundamentals Laboratory
The Programming and AI Fundamentals Laboratory provides foundational training in programming, algorithm development, and computational thinking. Students learn programming languages such as Python, C, Java, and R with a focus on problem-solving, logic building, and the basics of Artificial Intelligence and Machine Learning concepts essential for intelligent application development.
Data Science and RDBMS Laboratory
The Data Science and RDBMS Laboratory enables students to understand database design, data modeling, and data management using relational and non-relational database systems. Students gain practical experience in SQL, data preprocessing, data visualization, and handling large datasets for Machine Learning and analytics applications.
Networking and Cloud Computing Laboratory
The Networking and Cloud Computing Laboratory provides hands-on exposure to computer networks, cloud platforms, and distributed systems. Students learn networking protocols, cloud deployment, virtualization, cybersecurity basics, and cloud-based AI services through practical implementation and simulation tools.
Operating Systems and System Programming Laboratory
The Operating Systems and System Programming Laboratory focuses on practical implementation of operating system concepts such as process scheduling, memory management, synchronization, file systems, and system-level programming. Students also explore resource optimization techniques used in AI and high-performance computing environments.
Artificial Intelligence and Machine Learning Laboratory
This laboratory provides practical exposure to Artificial Intelligence, Machine Learning, and Deep Learning techniques. Students work with modern AI frameworks and tools to develop predictive models, neural networks, natural language processing applications, and intelligent systems using real-world datasets.
Advanced Computing and Intelligent Systems Laboratory
The Advanced Computing and Intelligent Systems Laboratory emphasizes advanced algorithms, data structures, computer vision, reinforcement learning, and intelligent application development. Students undertake project-based learning, research activities, and real-time AI solution development to enhance innovation, technical expertise, and industry readiness.
PEO's & PO's
Program Educational Objectives (PEOs)
PEO 1:To provide strong theoretical and practical foundations in Computer Science, Artificial Intelligence, and Machine Learning, enabling graduates to pursue higher education, entrepreneurship, research, and successful careers in industry.
PEO 2: To develop analytical thinking, problem-solving, innovation, and decision-making skills to design intelligent, efficient, and socially responsible AI-based solutions for real-world challenges.
PEO 3: To inculcate professional ethics, leadership qualities, teamwork, and lifelong learning attitudes to address global technological advancements and contribute positively to society and sustainable development.
Program Outcomes (POs)
PO 1: Engineering Knowledge:Apply the knowledge of mathematics, science, engineering fundamentals, Artificial Intelligence, Machine Learning, and Computer Science specialization to solve complex engineering problems.
PO 2: Problem Analysis:Identify, formulate, review research literature, and analyze complex computing and intelligent system problems using principles of mathematics, data science, and engineering sciences to arrive at valid conclusions.
PO 3: Design/Development of Solutions: Design and develop intelligent systems, software applications, and AI-based solutions for complex engineering problems with due consideration for public health, safety, societal, cultural, and environmental factors.
PO 4: Conduct Investigations of Complex Problems: Use research-based knowledge and modern research methods including experimentation, data analysis, interpretation, and synthesis of information to provide meaningful conclusions for AI and computing problems.
PO 5: Modern Tool Usage: Create, select, and apply appropriate modern engineering, programming, AI, Machine Learning, data analytics, and cloud-based tools for modelling, simulation, and solving complex engineering activities with an understanding of their limitations.
PO 6: The Engineer and Society: Apply contextual knowledge to assess societal, ethical, legal, cultural, cybersecurity, privacy, and safety issues relevant to Artificial Intelligence and professional engineering practice.
PO 7: Environment and Sustainability: Understand the impact of AI and computing solutions on society and the environment, and demonstrate the need for sustainable, energy-efficient, and socially responsible technological development.
PO 8: Ethics: Apply ethical principles and commit to professional ethics, data privacy, transparency, fairness, and responsibilities in the development and deployment of AI and Machine Learning systems.
PO 9: Individual and Team Work:Function effectively as an individual, and as a member or leader in diverse, multidisciplinary, and collaborative teams in computing and AI projects.
PO 10: Communication: Communicate effectively on complex engineering and AI activities through technical reports, research publications, presentations, documentation, and effective interaction with the engineering community and society.
PO 11: Project Management and Finance : Demonstrate knowledge of engineering management principles, entrepreneurship, finance, and project management, and apply these effectively in multidisciplinary AI and software development environments.
PO 12:Life-long Learning:Recognize the need for independent and lifelong learning to adapt to rapidly evolving technologies in Artificial Intelligence, Machine Learning, Data Science, and Computer Science.