
Computer Science & Engineering (AI & Machine Learning)
A specialised track for the machine-learning era.
CSE-AIML
Programme
240 seats
Sanctioned Intake
ML lifecycle
Track
Competition-driven
Practice
About the Department
The AI & Machine Learning specialisation focuses on the full lifecycle of intelligent systems — from data acquisition and feature engineering to model training, evaluation and deployment at scale.
Students work with contemporary toolchains and participate in Kaggle-style competitions, research reading groups and industry-mentored projects.
The programme is designed to make graduates immediately productive in ML engineering and data-science roles.
Vision
To be recognised for excellence in artificial intelligence and machine-learning education that translates directly into industry and research impact.
Mission
- 01Build deep competence in machine-learning theory and practice.
- 02Emphasise data-centric engineering, model evaluation and deployment.
- 03Encourage research publications, competitions and open-source contribution.
- 04Strengthen placement readiness through applied, portfolio-driven learning.
Programmes Offered
- CSE (AI & Machine Learning)300 seats
Total sanctioned intake: 300
PEO's, PSO's & PO's
Program Educational Objectives (PEOs)
PEO1
Achieve professional competency across the machine-learning lifecycle from data to deployment. (Professional Competency)
PEO2
Build successful careers as ML engineers, data scientists, entrepreneurs or researchers. (Successful Career Goals)
PEO3
Engage in lifelong learning and contribute to society through innovative, data-driven solutions. (Continuing Education and Contribution to Society)
Program Specific Outcomes (PSOs)
PSO1
Apply machine-learning theory, feature engineering and model evaluation to solve real-world data problems.
PSO2
Engineer and deploy scalable ML solutions using contemporary toolchains and MLOps practices.
Program Outcomes (POs)
Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.
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.
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.
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.
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.
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.
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.
Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.
Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
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.
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.
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.
Regulation and Syllabus
The department follows the JNTUA outcome-based curriculum, periodically revised by the Curriculum Development Cell in line with AICTE and NBA guidelines.
Regulation documents and the detailed semester-wise syllabus are available from the department office and the college Exam Portal.
Faculty Profile
Dr. V. Janardhan Babu
Professor & Head (CSE)
Ph.D
Dr. T. Sunil Kumar Reddy
Professor (Principal)
Ph.D
Dr. D. Nagaraju
Professor
Ph.D
Dr. B. Ramaganesh
Associate Professor
Ph.D
Dr. N. Srinivas Rao
Associate Professor
Ph.D
Dr. G. B. Hima Bindu
Associate Professor
Ph.D
M. Malli
Assistant Professor
M.Tech
P. Gopi Chand
Assistant Professor
M.Tech
B. Usha Bindu
Assistant Professor
M.Tech
M. Revathi
Assistant Professor
M.Tech
M. Munendra
Assistant Professor
M.Tech
K. H. Suhasini
Assistant Professor
M.Tech
A. Sharmila
Assistant Professor
M.Tech
Course Material
Subject-wise lecture notes, lesson plans, question banks, lab manuals and model papers are curated by the faculty and shared through the department's learning portal and class repositories.
Laboratory Facilities
Machine Learning Lab
60 systemsWorkstations with Python data-science stack, scikit-learn and TensorFlow.
Big Data Analytics Lab
30 systemsHadoop/Spark cluster tooling for large-scale data analytics.
Model Deployment & MLOps Lab
30 systemsContainerised deployment, CI/CD and model-serving environments.
Research Facilities
The specialisation encourages competition-driven and applied research across the ML lifecycle, supported by the R&D Cell.
Research Thrust Areas
Mentor Details
Faculty mentors guide each student under the institutional Mentor Program, emphasising portfolio building and competition participation.
Departmental Activities
- Guest lectures on applied machine learning
- Hands-on MLOps and analytics workshops
- Hackathons and Kaggle competitions
- Industry-mentored capstone projects
- Open-source contribution drives
Professional Bodies
Department Library
A dedicated departmental library supplements the central library with titles, reference volumes, previous question papers, project reports and subscriptions to technical journals for ready student and faculty access.
Career outcomes are supported by the campus-wide Training & Placement Cell — explore placements →