|| Sri. Devaraj Urs Educational Trust(R.) ||
Approved By AICTE, New Delhi | Affiliated to Visvesvaraya Technological University, Belagavi. Kodigehalli, Doddaballapur-561203,
Bengaluru Rural District, Karnataka, India.
The Department was started in the academic year 2022-23 to offer under graduate degree programme i.e., Bachelor of Engineering (BE) in Computer Science & Engineering (Artificial Intelligence and Machine Learning). AI&ML is a new and upcoming program in the area of computer science that is rapidly expanding its boundaries to variety of fields like healthcare, security, entertainment, education, autonomous transportation, intelligent robots, space exploration, speech processing, stock trading and many more. We have seen a lot of change in our lives because to artificial intelligence and its applications.
AI aims to artificially imitate human intelligence in order to instil intelligence into robots. An application of AI called machine learning (ML) aims to give machines the ability to learn on their own. Numerous career opportunities have been produced as a result of AI and ML, which are significant drivers of the digital transformation. There is a huge demand for skilled engineers trained in these technologies.
A degree from this programme is valuable and helps students get employment in their fields. This course assists in developing fundamental knowledge and skills in the areas of mathematics, network architectures, computer vision, programming, communication networks, and machine learning courses which enrich the student skills and knowledge for developing smart and intelligent application which delivers the needs of society and nation.
The department organizes training programmes for the staffs and students which helps themselves in technological advancements and present research findings. To increase the opportunity of placements to students and overall personality development, the department conducts and motivates them in soft skills training programmes, technical skill development activities, and initiatives on self-learning like certification courses through recognized online platforms (SWAYAM-NPTEL, Courseara, Udemy, edx etc..) and extracurricular activities under the umbrella of various associations.
Associate Professor and Head IQAC Coordinator
Dr. Manjunatha B.N is working as an Associate Professor & Head, Department of Computer Science & Engineering (Artificial Intelligence & Machine Learning), R.L. Jalappa Institute of Technology, Doddaballapur, Karnataka, India. He obtained Ph.D., in Computer Science & Engineering from Visvesvaraya Technological University (VTU), Belagavi, M.Tech in Computer Science & Engineering from SJCIT, Chikkaballapur & B.E in Information Science & Engineering from R L Jalappa Institute of Technology, Doddaballapur. He is having a Professional memberships in Institution Society for Technical Education (ISTE) & Associate Member of Institution of Engineers (AMIE). He has 14+ years of teaching experience. He published/presented research papers in various National /International journals of reputed and notable conferences. He has published 3 patents & one patent got a grant. He has written one Text books in academic. His area of interest is in Computer Network, Data Mining, IoT, Artificial Intelligence & Machine Learning & Context Computing. He has received Research Projects Funded by KSCST and VTU. He got an award for his outstanding & outreach activities. He is the recipient of “Smt. VijayaLakshmi & Sri. R L Jalappa Memorial Best Faculty award” (Special Category) for the year 2022-23 for overall Excellence in teaching & Institutional activities. Received “State Level Best Teacher Award” for the outstanding contribution in the Academics from the Challengers Youth Forum (R.), Doddaballapur -561 203 on 5th September 2022.
M1: To craft the students with Novel and Intellectual skills to capability in the field of Artificial Intelligence and Machine Learning.
M2: To train the students to have Professional career in the field of AI and ML and zeal for Higher Studies and Research.
PEO1: Graduates will have Prospective careers in the field of AI and ML.
PEO2: Graduates will have good Leadership Qualities, Self Learning abilities and zeal for higher studies and Research.
PEO3: Graduates will follow Ethical Practices and exhibit high level of professionalism by participating and addressing Technical, Business and Environmental challenges.
PSO1: Students will have the ability to understand analyse and demonstrate the knowledge of Human cognition, Artificial Intelligence, Machine Learning in terms of real world problems to meet the challenges of future.
PSO2: Students will have the knowledge of software, Hardware, Algorithms, Modelling Networking and Application Development.
PSO3: Students will have the ability to develop computational knowledge using Innovative tools and techniques to solve problems in the areas related to Machine learning and Artificial Intelligence.
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 modeling 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: 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.
The department is well equipped with laboratory facilities like the Data Structures, Algorithm Laboratory and the Machine Learning Laboratory. These laboratory facilities play a crucial role in providing students with hands-on experience and practical exposure to the concepts and technologies taught in the classroom. The laboratory is equipped with 65 computer systems with the necessary software tools required for implementing and testing academic laboratory experiments.
Data Structures and Algorithm Laboratory: This lab is designed to help students understand and implement various experiments of data structures and algorithms, and also Ability Enhancement Courses with practical approaches. It provides an environment for students to practice coding, analyze the efficiency of different algorithms, and solve complex problems.
Machine Learning Laboratory: The Machine Learning Laboratory is dedicated to providing students with practical experience in developing and applying machine learning algorithms. The lab is equipped with powerful computing resources and software tools for tasks such as data pre-processing, model training, and evaluation. Students can work on real-world datasets and projects to gain insights into machine learning techniques, experiment with different algorithms, and evaluate their performance.
