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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
SVPP students working on laptops in class
Computing · Est. 2021

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

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

  1. 01Build deep competence in machine-learning theory and practice.
  2. 02Emphasise data-centric engineering, model evaluation and deployment.
  3. 03Encourage research publications, competitions and open-source contribution.
  4. 04Strengthen placement readiness through applied, portfolio-driven learning.

Programmes Offered

  • CSE (AI & Machine Learning)300 seats

Total sanctioned intake: 300

Outcomes Framework

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)

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

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

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

Machine Learning Lab

60 systems

Workstations with Python data-science stack, scikit-learn and TensorFlow.

Big Data Analytics Lab

30 systems

Hadoop/Spark cluster tooling for large-scale data analytics.

Model Deployment & MLOps Lab

30 systems

Containerised deployment, CI/CD and model-serving environments.

R & D

Research Facilities

The specialisation encourages competition-driven and applied research across the ML lifecycle, supported by the R&D Cell.

Research Thrust Areas

Machine LearningBig Data AnalyticsMLOps & DeploymentPredictive ModellingData-Centric AI
Student Support

Mentor Details

Faculty mentors guide each student under the institutional Mentor Program, emphasising portfolio building and competition participation.

Engagement

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
Affiliations

Professional Bodies

CSI Student ChapterIEEEISTE 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
ML EngineerData ScientistAnalytics EngineerResearch AssociateAI Product Developer

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