Announcements:
The Department of Artificial Intelligence and Data Science is established in the year 2021, offers an undergraduate programme BE under the affiliation of Visvesvaraya Technological University, Belgaum.
We, CMRIT believe in inculcating technical culture among students for their betterment in life and to face the challenging and competitive real world outside. Our faculty deliver the best learning content through practical components by means of experiments, miniprojects, placement training and entrepreneurial projects which will improve students Higher Order Thinking and support Outcome Based Education. We prepare and practice a high standard Teaching Learning Process to bestow university curriculum along with the encouragement for lifelong learning through MOOCs, and research. To perform high in placements and reach our targets, various career development programmes, workshops, seminars and Hackathons are being conducted throughout the academic year.
The main motto of the department is to bring out the technical talent of the younger generation and mould them holistically in such a way that they face the external world with prompt interpersonal and problem-solving skills. In order to make the students industry ready with complete personality and competency, we improve on tying up with various industry and conduct activities like industry visits, technical talks, and real-time projects to polish the students’ technical and soft skills.
A multidisciplinary department with core and elective subjects covering the entire spectrum of Engineering. Following are the core and unique strengths of the department :
1. Highly qualified faculty team, including members with degrees from internationally recognized universities.
2. Continuous faculty knowledge upgradation through MOOCs and professional certification platforms such as NPTEL, Coursera, and other online learning initiatives.
3. Strong research culture, with faculty members actively contributing through patents and research publications in reputed journals and conferences.
4. Industry-oriented learning supported by two Professors of Practice, bringing real-world industry expertise into the classroom.
5. Center of Excellence in Video Analytics, functioning under the department and promoting advanced research and practical applications in AI and Computer Vision.
6. Interdisciplinary projects undertaken by faculty and students to address real-world challenges using Artificial Intelligence and Data Science.
7. Entrepreneurial ecosystem, with both faculty and students involved in startup initiatives.
8. Outstanding student achievements, including winning International Level / National level hackathons and technical competitions.
9. Global recognition of students, with two students serving as Google Brand Ambassadors.
10. Active student technical clubs, including the Torvalds Club and CodeChef Club, which provide platforms for coding practice, hackathons, and technical events.
11. Social impact initiatives, including an MoU with an NGO to apply AI and Data Science solutions for societal benefit.
PEO1: Graduate would be successful in their profession with strong basics in engineering, science, and technology.
PEO2: Graduate would be able to formulate, analyze, design, develop and test Artificial Intelligence and Data science based solutions for actual business problems.
PEO3: Graduate would be able to follow standard practices in project building and demonstrate valid managerial skills.
PEO4: Graduate would be capable of becoming an entrepreneur or accomplishing higher studies.
PEO5: Graduate would be committed to adhere ethical values and exhibit social responsibility.
1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals and an engineering specialization to the solution of complex engineering problems.
2. Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences and engineering sciences.
3. Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.
4. Conduct investigations of complex problems: Use research–based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
5. Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modelling to complex engineering activities with an understanding of the limitations.
6. The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
7. Environment and sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
8. Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.
9. Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
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.
11. Project management and finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
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.
PSO1: To understand the given problem and formulate the smart solutions with fundamental engineering knowledge and appropriate analytical and technical skills.
PSO2: To be able to propose and implement software applications with the concepts of data structures, analysis of algorithms, DBMS, cloud computing and applications, machine learning and data analytics related tools and techniques.
PSO3: To build an artificial Intelligence and data visualization based, secured business solutions by means of Fuzzy logic and its applications, Deep Learning , business Intelligence, Blockchain, soft and evolutionary computing and Data Security and Privacy subjects.
PSO4: To apply mathematical notion in computational tasks and software project development to produce quality products.
The Data Structures Lab provides students with hands-on experience in designing and implementing efficient algorithms using arrays, linked lists, stacks, queues, trees, graphs, and hashing techniques. Students learn problem-solving, algorithm analysis, and optimization through programming exercises and real-world applications. The lab is equipped with modern computing systems and development tools that support coding, debugging, and performance evaluation.
The Machine Learning Lab focuses on the design, development, and evaluation of predictive models using real-world datasets. Students perform data preprocessing, feature engineering, model training, validation, and performance analysis using supervised and unsupervised learning techniques. The lab provides hands-on exposure to popular machine learning frameworks and tools, enabling students to develop solutions for classification, regression, clustering, and recommendation systems.
The Big Data Analytics Lab equips students with the knowledge and practical skills required to process, manage, and analyze large-scale datasets. The lab covers distributed computing concepts, data storage, data mining, and real-time analytics using technologies such as Hadoop, Spark, Hive, and related ecosystem tools. Students gain experience in handling high-volume and high-velocity data, enabling them to derive meaningful insights for business, research, and decision-making applications.
The DevOps Lab provides practical exposure to modern software development and deployment practices. Students learn version control, continuous integration, continuous delivery, containerization, infrastructure automation, and cloud deployment using tools such as Git, Jenkins, Docker, Kubernetes, and Ansible. The lab emphasizes automation, collaboration, and scalability, enabling students to build and manage reliable software delivery pipelines.
The Artificial Intelligence Lab enables students to explore intelligent problem-solving techniques and develop AI-based applications. The laboratory covers search algorithms, knowledge representation, reasoning systems, expert systems, planning, and intelligent agents. Students implement AI models using industry-standard frameworks and gain practical experience in building systems capable of decision-making and automation.
The Python Programming Lab introduces students to programming fundamentals using Python, one of the most widely used languages in Artificial Intelligence and Data Science. The lab covers data types, control structures, functions, object-oriented programming, file handling, and Python libraries. Students gain practical exposure to scientific computing, data visualization, and application development using tools such as NumPy, Pandas, and Matplotlib.
Students should have completed higher secondary (12th Standard) education with the subjects and eligibility requirements prescribed by the competent authorities. Admission is generally through recognized entrance examinations and counselling as per institutional and regulatory norms.
The programme includes core computing subjects along with areas such as mathematics and statistics, data structures, databases, cloud programming, web applications development, artificial intelligence, machine learning, data analytics, deep learning, Generative AI, AgenticAI and other emerging technologies of computing, depending on the applicable curriculum.
