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Admissions 2026–27 are now open across all programmesAutonomous · NAAC & NBA Accredited · Affiliated to JNTUA80%+ placements · 100+ recruiters every seasonCall the Admissions Office — +91 93905 05457Admissions 2026–27 are now open across all programmesAutonomous · NAAC & NBA Accredited · Affiliated to JNTUA80%+ placements · 100+ recruiters every seasonCall the Admissions Office — +91 93905 05457
Students at work in the SVPP computer laboratory
Computing · Est. 2020

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

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

  1. 01Ground students in mathematics, statistics and the foundations of machine intelligence.
  2. 02Provide hands-on exposure to modern AI frameworks, data pipelines and MLOps.
  3. 03Promote responsible, explainable and ethical AI practice.
  4. 04Connect learning to live industry problems through capstone projects.

Programmes Offered

  • CSE (Artificial Intelligence)240 seats

Total sanctioned intake: 240

Outcomes Framework

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)

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, review research literature, and analyze 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 the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.

PO4

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.

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 the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.

PO7

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.

PO8

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

PO9

Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary 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 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.

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.

Curriculum

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.

Exam Portal & Syllabus →
People

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

Resources

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.

Infrastructure

Laboratory Facilities

AI & Machine Learning Lab

60 systems

Intel i5/i7 workstations with Python, scikit-learn, TensorFlow and PyTorch toolchains.

Deep Learning (GPU) Lab

30 systems

GPU-accelerated workstations for training deep neural networks and computer-vision models.

Data Engineering Lab

30 systems

Big-data and data-pipeline tooling for ingestion, transformation and feature engineering.

Computer Vision Lab

30 systems

Image and video processing stations with OpenCV and vision frameworks.

R & D

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

Machine LearningDeep LearningNatural Language ProcessingComputer VisionResponsible & Explainable AI
Student Support

Mentor Details

Students are mentored under the institutional Mentor Program, with faculty mentors guiding academics, project portfolios and AI specialisation pathways.

Engagement

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
Affiliations

Professional Bodies

IEEE Computational Intelligence SocietyCSI Student ChapterISTE Student Chapter
Knowledge

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.

Where Graduates Go
Machine Learning EngineerAI EngineerData ScientistNLP EngineerMLOps Engineer

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