Artificial Intelligence (AI) is a computing concept that enables a machine to think and solve complex problems as we humans do with our natural intelligence. AI is the next phase of the industrial revolution and has established itself firmly in our society. Almost all branches of industry have been affected by the ongoing transformation through its algorithms.

Machine Learning explores the analysis and construction of algorithms that can learn from and make predictions on data. ML has proven valuable because it can solve problems at a speed and scale that cannot be duplicated by the human mind alone. With massive amounts of computational ability behind a single task or multiple specific tasks, machines can be trained to identify patterns in and relationships between input data and automate routine processes.

The AI & ML has opened up exciting new opportunities for interdisciplinary work across many fields including computer science, mathematics, statistics, and information science from which it draws foundational knowledge and the current demand for a career in AI & ML is considerable and growing daily.

The B.Tech (Artificial Intelligence and Machine Learning) course addresses this transformation by providing you as a student with the broad and in-depth skills required to work with and develop AI. You will be trained how to obtain, process and store enormous amounts of data, which is the root of AI and development processes.

HEAD OF THE DEPARTMENT

Dr.G. Arun Sampaul Thomas M.E., Ph. D (CSE)., M.I.S.T.E., M.I.E.T.

Received B.E. degree in Information Technology, and M.E. and Ph.D. degrees in Computer Science and Engineering from Anna University, Chennai, in 2006, 2010, and 2018, respectively. He is currently working as HOD and Associate Professor in J.B. Institute of Engineering and Technology, Hyderabad, India. He has overall teaching experience more than ten years. His research areas include Data Science, Machine Learning, Computer networks, Big Data Analytics, and IoT. He Presented papers in five international conferences and various national conferences. His papers were published in various reputed international journals.  He attended several seminars, and workshops. He is an active life member of ISTE & IET, technical education fellowships.

Phone: 9585511808​     E-mail:  hod.ai_ml@jbiet.edu.in

VISION:

To become a Centre of Excellence in AI&ML, shaping professionals obliging to the research and proficient needs of national and international organizations and to bring up innovative ideas to solve real time problems through continuous research, innovation, and industry steered curriculum.

MISSION:

M1: To transform the students into technologically proficient and help them to absorb the innovative spirit.

M2: To impart premier quality, skill-based and value-based education to the students in the field of Artificial Intelligence and Machine Learning.

M3: To identify corporate requirements and enrich the students’ expertise with a strong theoretical and practical backdrop having an emphasis on hardware and software development with social ethics.

Program Educational Objectives (PEOs)

PEO1

To Formulate, analyse and solve Engineering problems with strong foundation in Mathematical, Scientific, Engineering fundamentals and modern AI&ML practices through advanced curriculum.

PEO2

Analyze the requirements, realize the technical specification and design the Engineering solutions by applying artificial intelligence and machine learning theory and principles.

PEO3

Demonstrate technical skills, competency in AI&ML and promote collaborative learning and team work spirit through multi-disciplinary projects and diverse professional activities along with imbibing soft skills and ethics.

Program Outcomes and Program Specific Outcomes of AI&ML Department (POs & PSOs)

PO1: Engineering Knowledge: Apply the knowledge of mathematics, science, engineering fundamentals and an engineering specialization to the solution of complex engineering problems.

PO2: Problem Analysis: Identify, formulate, research literature, and analyse complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.

PO3: 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 and safety, cultural, societal, and environmental considerations.

PO4: Conduct investigations of complex problems: using research-based knowledge and research methods including design of experiments, analysis and interpretation of data and synthesis of information to provide valid conclusions.

PO5: 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.

PO6: 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 professional engineering practice.

PO7: Environment and Sustainability: Understand the impact of professional engineering solutions in societal and environmental contexts and demonstrate knowledge of and need for sustainable development.

PO8: Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of engineering practice.

PO9: Individual and Teamwork: Function effectively as an individual, and as a member or leader in diverse teams and in multi-disciplinary settings.

PO10: 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.

PO11: Project Management and Finance: Demonstrate knowledge and understanding of 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.

PO12: 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. Any signatory needs to provide an overview of its learning outcomes and confirm that compliance of programs.

PSO1

The ability to understand, analyse and demonstrate the knowledge of human cognition, Artificial Intelligence, Machine Learning and data science in terms of real world problems to meet the challenges of the future.

PSO2

The ability to develop computational knowledge and project development skills using innovative tools and techniques to solve problems in the areas related to Deep Learning, Machine learning, Artificial Intelligence.

II B.Tech II Semester

Machine Learning Mavericks Club

Exploring, Learning, Innovating Responsibly with Machine Learning

Vision:

To create a community of learners who are passionate about exploring and implementing machine learning technologies to solve real-world problems and drive innovation.

Mission:

1. To provide a platform for students to learn and implement machine learning technologies in real-world scenarios.
2. To organize technical workshops, seminars, and hackathons to enhance the technical skills of members.
3. To encourage members to participate in machine learning competitions and projects.
4. To collaborate with industry experts and academia to bring real-world experiences and knowledge to the club.
5. To foster a culture of innovation and creativity in the field of machine learning and its applications.
6. To provide opportunities for members to network with like-minded individuals and industry professionals.
7. To promote the use of machine learning technologies for social good and community development.

(Overall, the Machine Learning Mavericks student club aims to provide a supportive community for students to explore, learn, and apply machine learning technologies to solve real-world problems and contribute to the field of AI and ML.)

Faculty Co-ordinator:

Mr. S. Sathish Kumar (Ph.D.), Assistant Professor, AI&ML Department

Student Co-ordinators:

AI&ML - Section A

Mr. B. Chandrashekar

Mr. Syed Faizanuddin

AI&ML - Section B

Mr. Om Verma

Mr. Koushik Pingilli

Achievements of the Faculty

Seminars / Workshops / Training Programs organized

Seminars/ Workshops/ Conference/ FDPs attended by faculty

Expert lectures organized for students:

Guest lectures organized for students:

Prizes won by the students in Paper contests:

Papers presented by the students in Conferences:

Student Club Activities:

Industrial Visits

Internships

Research Activities - Thrust areas of research of the department.

Papers Published by faculty in Journals

Books/ Chapters published by the faculty members

Patents Filed/ Published/ Granted

Innovations in Teaching and Learning

Introduction: The Department of AI&ML is committed to innovative and effective teaching practices that help students to better understand and apply the concepts they are learning, as well as develop important skills that are necessary for success in the field. In this report, we will discuss the innovative teaching and learning techniques followed by the department.

Project-Based Learning: The Department of AI&ML uses project-based learning as a teaching method that focuses on students working on a long-term project or problem that is based on real-world scenarios. This approach allows students to apply their knowledge and skills in a practical way, while also developing critical thinking and problem-solving skills.

For example, students in the B.Tech AI&ML program may work on a project where they are asked to analyse a large dataset and develop a machine learning algorithm that can make predictions based on the data. Students are provided with real-world datasets and are required to work in teams to develop and test their algorithms. This approach not only helps students to apply the concepts they are learning in the classroom to real-world scenarios, but also helps them to develop important teamwork and communication skills.

Flipped Classrooms: The Department of AI&ML also uses flipped classrooms as a teaching method. In a flipped classroom, students learn the course material outside of class through online lectures, videos, or readings, and then come to class to engage in activities, discussions, and problem-solving exercises. This approach can help students develop a deeper understanding of the material and allow for more interactive and collaborative learning.

For example, students in the B.Tech AI&ML program may be provided with online video lectures or readings on a specific topic, and then come to class to engage in discussions and problem-solving exercises related to the topic. This approach not only helps students to develop a deeper understanding of the material, but also provides them with the opportunity to engage in interactive and collaborative learning, which can enhance their critical thinking and problem-solving skills.

Active Learning: The Department of AI&ML also uses active learning as a teaching method. Active learning involves engaging students in the learning process through hands-on activities, discussions, and problem-solving exercises. This approach can help students develop a deeper understanding of the material and enhance their critical thinking and problem-solving skills.

For example, students in the B.Tech AI&ML program may engage in hands-on activities, such as building and testing machine learning algorithms or working on real-world projects that require data analysis and prediction. Students may also engage in discussions and problem-solving exercises related to the course material. This approach not only helps students to develop a deeper understanding of the material, but also provides them with the opportunity to engage in interactive and collaborative learning, which can enhance their critical thinking and problem-solving skills.

Blended Learning: Blended learning involves combining online learning with traditional classroom methods. This approach can provide students with flexibility and convenience, as well as access to a variety of multimedia materials and interactive simulations. In a blended learning environment, students can work at their own pace and have the opportunity to engage in interactive and collaborative learning.

Peer Teaching: Peer teaching involves having students teach and learn from each other. This approach can help students develop leadership, communication, and critical thinking skills, while also providing opportunities for peer feedback and support.

Simulation-Based Learning: Simulation-based learning involves using computer simulations to teach concepts and skills. This approach can provide students with a safe and controlled environment to practice and experiment with complex concepts, such as artificial intelligence and machine learning.

Gamification: Gamification involves using game elements, such as points, badges, and leaderboards, to motivate and engage students in learning. This approach can make learning more enjoyable and immersive, while also providing students with instant feedback and recognition for their achievements.

Personalized Learning: Personalized learning involves tailoring the learning experience to the individual needs and preferences of each student. This approach can help students to learn at their own pace and in a way that suits their learning style, while also providing opportunities for personalized feedback and support.

Inquiry-Based Learning: Inquiry-based learning involves having students explore and discover information through research and investigation. This approach can help students to develop critical thinking and problem-solving skills, as well as a deeper understanding of the subject matter.

Service Learning: Service learning involves having students apply their knowledge and skills to real-world problems through community service or volunteer work. This approach can help students to develop empathy, social responsibility, and a deeper understanding of the impact of their work on society.

Conclusion: In conclusion: The Department of AI&ML is committed to innovative and effective teaching practices that help students to better understand and apply the concepts they are learning, as well as develop important skills that are necessary for success in the field. Incorporating these teaching and learning practices into the B.Tech AI&ML curriculum can provide students with a comprehensive and engaging learning experience that prepares them for success in their academic pursuits and careers. By using a combination of these practices, the department can cater to the diverse learning needs and preferences of its students, while also creating a collaborative and supportive learning environment.