
Computer Science & Engineering (Artificial Intelligence)
Engineering intelligence — from neural foundations to deployment.
CSE-AI
Programme
240 seats
Sanctioned Intake
ML · NLP · Vision
Focus
GPU AI lab
Compute
About the Department
The Artificial Intelligence programme was introduced to meet the surging national demand for AI talent, blending core computer-science rigour with specialised study in learning systems.
Coursework spans machine learning, deep learning, natural language processing, computer vision and the engineering of production AI — with dedicated GPU-backed lab time.
Capstone projects partner students with real datasets and problem statements, building portfolios that recruiters recognise.
Vision
To produce engineers who design, build and responsibly deploy artificial-intelligence systems that serve society.
Mission
- 01Ground students in mathematics, statistics and the foundations of machine intelligence.
- 02Provide hands-on exposure to modern AI frameworks, data pipelines and MLOps.
- 03Promote responsible, explainable and ethical AI practice.
- 04Connect learning to live industry problems through capstone projects.
Programmes Offered
- CSE (Artificial Intelligence)240 seats
Total sanctioned intake: 240
PEO's, PSO's & PO's
Program Educational Objectives (PEOs)
PEO1
Attain professional competency in artificial intelligence through a strong foundation in mathematics, computing and learning systems. (Professional Competency)
PEO2
Excel in one's career as an AI engineer, data scientist, entrepreneur or through higher studies. (Successful Career Goals)
PEO3
Adapt to rapidly evolving AI technologies and contribute to society through responsible innovation. (Continuing Education and Contribution to Society)
Program Specific Outcomes (PSOs)
PSO1
Apply the principles of machine learning, deep learning and data engineering to analyse data and build intelligent systems.
PSO2
Design, evaluate and deploy responsible, explainable AI solutions using modern frameworks and platforms.
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
S. Jeelan
Assistant Professor
M.Tech
N. Muni Sankar
Assistant Professor
M.Tech(Ph.D)
M. Ranjith Kumar Reddy
Assistant Professor
M.Tech(Ph.D)
P. Suresh
Assistant Professor
M.Tech
K. Pavani
Assistant Professor
M.Tech(Ph.D)
M. Guravaih Yadav
Assistant Professor
M.Tech
T. Srivani
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
AI & Machine Learning Lab
60 systemsIntel i5/i7 workstations with Python, scikit-learn, TensorFlow and PyTorch toolchains.
Deep Learning (GPU) Lab
30 systemsGPU-accelerated workstations for training deep neural networks and computer-vision models.
Data Engineering Lab
30 systemsBig-data and data-pipeline tooling for ingestion, transformation and feature engineering.
Computer Vision Lab
30 systemsImage and video processing stations with OpenCV and vision frameworks.
Research Facilities
Research activity centres on applied machine learning and the responsible deployment of AI, supported by the institutional R&D Cell.
Research Thrust Areas
Mentor Details
Students are mentored under the institutional Mentor Program, with faculty mentors guiding academics, project portfolios and AI specialisation pathways.
Departmental Activities
- Guest lectures by AI researchers and industry practitioners
- Workshops on ML frameworks, MLOps and generative AI
- Kaggle-style competitions and datathons
- Capstone projects with real datasets
- AI reading groups and paper-presentation seminars
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 →