Computer Science and Engineering (AIML)

Why Choose CSE AI & ML at ACE?

The Department of Computer Science and Engineering (Artificial Intelligence & Machine Learning) at ACE was established to address the rising demand for intelligent systems professionals, offering a robust four-year undergraduate programme with a current intake of 180 students. This specialised B.Tech programme blends strong computing fundamentals with practical training in AI, machine learning, and data-driven technologies. With advanced labs, state-of-the-art GPU cluster facilities, and a dedicated Centre for AI & ML, students gain real-time exposure to tools like Python, TensorFlow, and cloud platforms.

Backed by expert faculty and a structured placement training program, the department equips graduates for roles in data science, automation, and intelligent systems—while nurturing innovation, ethics, and lifelong learning.

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Accreditation

Vision & Mission

Our Vision

To be a centre of excellence in Artificial Intelligence and Machine Learning education, research, and innovation, producing globally competent professionals who drive the future of intelligent systems.

Our Mission

Imparting quality Technical Education to young Electronics and Communication Engineer by providing them

Deliver an industry-relevant curriculum with strong foundations in AI, ML, and core computing principles through advanced labs and expert faculty.
Prepare students for successful careers and higher studies in AI-related domains through hands-on learning and problem-solving.
Encourage innovation, research, and entrepreneurship in intelligent technologies, aligned with global standards.
Instill lifelong learning, ethical values, and societal responsibility among future AI professionals.

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Curriculum Syllabus

R22 Syllabus

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AI & ML I & II year Syllabus

R24 Syllabus

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II/I and II/II

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I-II, II-II, III-II & IV-II

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AI & ML III year Syllabus

R25 Syllabus

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I-I, I-II, II-I, II-II, III-I, III-II, IV-I & IV-II

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AI & ML IV year Syllabus

Program Education Objectives

Program Educational Objectives (PEOs)

PEO 1: To prepare students for successful careers in CSE (Artificial Intelligence and Machine Learning) by providing training to excel in competitive examinations, pursue higher education, and secure employment.
PEO 2: To provide students with a broad-based curriculum, firmly grounded in Computer Science and Engineering, Applied Mathematics, and Sciences. To impart high-quality technical skills for designing, modelling, analysing, and solving critical problems with global competence.
PEO 3: To inculcate professional, social, and ethical values, along with effective communication skills and entrepreneurial practices, for the holistic growth of students.
PEO 4: To create an academic environment for Computer Science and Engineering students that fosters involvement in professional bodies, encourages a multidisciplinary approach, and promotes lifelong learning.
PEO 5: To develop research aptitude among students, enabling them to carry out research in cutting-edge technologies, solve real-world problems, and provide technical consultancy services.

Knowledge and Attitude Profile (WK)

WK1: A systematic, theory-based understanding of the natural sciences applicable to the discipline and awareness of relevant social sciences.
WK2: Conceptually-based mathematics, numerical analysis, data analysis, statistics and formal aspects of computer and information science to support detailed analysis and modelling applicable to the discipline.
WK3: A systematic, theory-based formulation of engineering fundamentals required in the engineering discipline.
WK4: Engineering specialist knowledge that provides theoretical frameworks and bodies of knowledge for the accepted practice areas in the engineering discipline; much is at the forefront of the discipline.
WK5: Knowledge, including efficient resource use, environmental impacts, whole-life cost, reuse of resources, net zero carbon, and similar concepts, that supports engineering design and operations in a practice area. WK6: Knowledge of engineering practice (technology) in the practice areas in the engineering discipline.
WK7: Knowledge of the role of engineering in society and identified issues in engineering practice in the discipline, such as the professional responsibility of an engineer to public safety and sustainable development.
WK8: Engagement with selected knowledge in the current research literature of the discipline, awareness of the power of critical thinking and creative approaches to evaluate emerging issues.
WK9: Ethics, inclusive behavior and conduct. Knowledge of professional ethics, responsibilities, and norms of engineering practice. Awareness of the need for diversity by reason of ethnicity, gender, age, physical ability etc. with mutual understanding and respect, and of inclusive attitudes

Program Outcomes (POs)

Engineering Knowledge
Apply principles of computing, mathematics, and AI techniques to solve complex real-world problems.
Problem Analysis
Identify and analyse data-driven challenges using algorithmic, statistical, and model-based approaches.
Design/Development of Solutions
Design intelligent systems and ML models that meet user needs within ethical, legal, and societal boundaries.
Investigation of Complex Problems
Use research methodologies to explore, evaluate, and refine data models and AI solutions.
Modern Tool Usage
Employ modern tools such as TensorFlow, Python, R, and cloud platforms for AI/ML system development and evaluation.
The Engineer and Society
Assess the broader impact of AI systems on privacy, fairness, and societal outcomes.
Environment and Sustainability
Promote responsible AI practices that support sustainable and inclusive technological development.
Ethics
Commit to ethical AI development, transparency, and responsible decision-making.
Individual and Team Work
Function effectively in multidisciplinary AI/ML teams and collaborative research projects.
Communication
Present complex AI-driven insights clearly through reports, dashboards, and visualizations.
Project Management and Finance
Apply project management tools and methods in developing scalable and impactful AI solutions.
Lifelong Learning
Pursue continuous learning in rapidly evolving AI and machine learning fields.

Program Specific Outcomes (PSOs)

Design and implement machine learning models using industry-standard frameworks to solve real-time problems across sectors.
Apply AI techniques such as natural language processing, deep learning, and computer vision in practical applications.
Demonstrate ethical responsibility and domain awareness in deploying intelligent systems at scale.

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Faculty & Research

Dr. Kavitha Soppari

HoD & Professor

Mr. Daram Narasimha Rao

Associate Professor

Mr. M Mahender

Associate Professor

Mr. M Ramesh

Associate Professor

Mrs. K Swetha Sailaja

Assistant Professor

Mrs. Jangam Bhargavi

Assistant Professor

Mr. M A Kalam

Assistant Professor

Mrs. Panchagnula Kamakshi Thai

Assistant Professor

Mr. Somagani Venkatesh

Assistant Professor

Mrs. M P Nisha

Assistant Professor

Mr. Konka Kishan

Assistant Professor

Mr. Shashank Tiwari

Assistant Professor

Mr. B. Avinash

Assistant Professor

Mr. Ragi Rajesh

Assistant Professor

Mr. Chitoor Venkat Rao Ajay Kumar

Assistant Professor

Mrs G.Monika

Assistant Professor

Mrs B.Suvidha

Assistant Professor

Mr. Sumit Kumar

Assistant Professor

Dr. Atul Kumar Ramotra

Associate Professor

Mrs. Ananthoji Veena Sri

Assistant Professor

Mrs B.Manasawini

Assistant Professor

Mr. Ch. Vamsi Krishna

Associate Professor

Mrs. Yerpula Vanitha

Assistant Professor

Mr. Durgunala Ranjith

Assistant Professor

Mrs. Jaya Sri Sai Kalluri

Assistant Professor

Ms. C. J. Aparna

Assistant Professor

Mr. Sasiram Anupoju

Assistant Professor

Mr. Harsh Chandra

Assistant Professor

Ms. Amisha Raj

Assistant Professor

Dr. CH. Kishore Kumar

Assistant Professor

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Infrastructure & Labs

The department houses cutting-edge facilities for immersive learning and research:

AI & ML Lab: Equipped with GPUs, development frameworks (TensorFlow, PyTorch), and real-time project support for training ML models.

Data Science Lab: Offers tools for data preprocessing, visualization, and analytics using platforms like R, Python, and Power BI.

Cloud Computing & Big Data Lab: Enables AI integration with cloud platforms (AWS, Azure) and scalable data solutions.

Research & Innovation Hub: A dedicated space for capstone projects, research papers, and AI product development under expert mentorship.

Explore Labs

92+

Campus Interviews Conducted

156+

Campus Interviews Conducted

31+

Campus Interviews Conducted

500+

Campus Interviews Conducted

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Placements Highlights – 2021 – 25

72

Campus Interviews Conducted

9

Programs Available for the Students

46.38L

Highest Package 46.38LPA in Amazon

434

Successful Campus Placements

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Advisory Board Members

Head of the Department

Dr. S. Kavitha

Head of the Department Chairman

Dr.K Adi Narayana Reddy

Assoc Prof. of DS and AI , ICFAI, Foundations of Higher Education Subject Experts from outside JNTUH

Ms. Nallabelli Bhavani

Software Engineer , ZenQ Alumni

Dr. K. Shahu Chatrapati

Professor & Addl. Controller of Examinations, CSE, JNTUH-UCEJ, Hyderabad, JNTUH Nominee

Mr.B. G. Srinivas

Senior Software Manager, ORACLE, Hyderabad Expert on Trending Technology

Dr.B.Sujatha

Assistant Professor of CSE Osmania University, Hyd
Subject Experts from outside JNTUH

Mr. Durga Naveen Kandregula

Founder & CEO, Coign Consultants Pvt Ltd. Industry/ Corporate Sector Representative