CSE(AI&ML) DEPARTMENT

CSE(AI&ML) Department

Department of Computer Science Engineering(AI & ML)

faculty photo

Dr. M. Lavanya

HOD & ASSOCIATE PROFESSOR

MCA, M.Tech, Ph.D

Department Profile

The Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning) at Sri Venkateswara College of Engineering and Technology (SVCET), Chittoor, Andhra Pradesh, is a leading academic unit with academic autonomy, offering a robust curriculum tailored to emerging technologies. Established in 2020 with an initial intake of 60 students, the department has expanded rapidly, with an enhanced intake of 180 students in 2021. The four-year B.Tech program specializes in Artificial Intelligence and Machine Learning (AI&ML), emphasizing hands-on learning, project-based education, and industry-focused internships. Students are trained in advanced topics such as robotics, cognitive systems, machine learning, pattern recognition, intelligent systems, and deep learning, along with a strong foundation in core computer science principles, equipping them to develop innovative solutions for real-world challenges.

The department is headed by Dr. M. Lavanya, an accomplished academician and researcher with over 18 years of teaching and research experience. Her expertise spans Data Mining, Web Mining, Machine Learning, and Data Analytics, with 25 research publications and four patents, including two in Artificial Neural Networks. Under her leadership, the department fosters a vibrant learning environment by organizing technical events, clubs, and hackathons to enhance student engagement and innovation. The department's mission is to prepare future engineers with the knowledge and skills to excel in the rapidly evolving field of Artificial Intelligence and Machine Learning.

Our Objectives

  • To provide a strong foundation in Artificial Intelligence and Machine Learning by equipping students with advanced knowledge in areas such as robotics, cognitive systems, deep learning, and intelligent systems.
  • To promote hands-on learning and practical exposure through project-based education, industry internships, and experiential learning, enabling students to solve real-world problems effectively.
  • To foster innovation and research by encouraging students and faculty to engage in cutting-edge research, technical events, hackathons, and collaborative projects in AI and ML.
  • To develop students' technical and analytical skills to design intelligent systems capable of understanding, analyzing, and performing tasks efficiently using modern AI and ML technologies.
  • To prepare students for diverse career opportunities and higher studies in AI and ML by offering a comprehensive curriculum that integrates core computer science principles with emerging technologies.
  • To nurture leadership, teamwork, and ethical values by providing a student-centric learning environment and opportunities for participation in clubs, competitions, and interdisciplinary collaborations.

Department Strength

  • ✔ Academic Autonomy
  • ✔ Comprehensive Curriculum
  • ✔ Experienced Leadership and Faculty
  • ✔ Hands-On Learning Opportunities
  • ✔ Rapid Growth and High Demand
  • ✔ Industry-Focused Initiatives
  • ✔ Cutting-Edge Research and Innovation

Courses Offered

Level Course Intake
Under Graduate CSE (Artificial Intelligence & Machine Learning) B.Tech 180

Vision and Mission

🎯 Vision

To achieve excellent standard of quality education by using latest tools in Artificial Intelligence and disseminating innovations to relevant areas.

🚀 Mission

  • To develop professionals who are skilled in Artificial Intelligence and Machine Learning.

  • Impart rigorous training to generate knowledge through the state-of-the-art concepts and technologies in Artificial Intelligence and Machine Learning

  • Establish centers of excellence in leading areas of computing and artificial intelligence to inculcate strong ethical values, innovative research capabilities and leadership abilities in the young minds to work with a commitment to the progress of the nation.

Program Outcomes


Program Educational Objectives (PEOs) under R23 Regulations

On successful completion of the Program, the graduates of B. Tech. CSE(AI & ML) Program will be able to:

PEO Code Outcome
PEO-1 To be able to solve wide range of computing related problems to cater to the needs of industry and society.
PEO-2 Enable students to build intelligent machines and applications with a cutting-edge combination of machine learning, analytics and visualization
PEO-3 Produce graduates having professional competence through life-long learning such as advanced degrees, professional skills and other professional activities related globally to engineering & society

Program Specific Outcomes (PSOs) under R23 Regulations

On successful completion of the Program, the graduates of B. Tech. CSE(AI & ML) Program graduates will be able to:

PSO Code Outcome
PSO-1 Should have an ability to apply technical knowledge and usage of modern hardware and software tools related AI and ML for solving real world problems
PEO-2 Should have the capability to develop many successful applications based on machine learning methods, AI methods in different fields, including neural networks, signal processing, and data mining.

CSE(AI&ML) Faculty List

S.No

Name of the Faculty

Designation

Qualification

Experience (in Years)

1

Dr. M. LAVANYA

HoD & PROFESSOR

MCA, M.TECH, PhD

20

2

Dr. PRINCE W

PROFESSOR

M.Tech(IT), Ph.D

12

3

Dr. K. NANDHAKUMAR

ASSOCIATE PROFESSOR

MCA, M.Tech, Ph.D

15 

4

Dr. SWAROOP NAMBAKAM

ASSOCIATE PROFESSOR

M.Tech, Ph.D

8.6

5

Mr.GEORGE SEBASTIAN

ASSOCIATE PROFESSOR

M.Tech,

9.4

6

Dr. E. NAGARJUNA CHOWDARY

ASSOCIATE PROFESSOR

M.Tech, Ph.D

12.6

7

Dr. VASANTHAN G

ASSOCIATE PROFESSOR

M.Tech, Ph.D

9.8

8

Dr. MADHULIKA

ASSOCIATE PROFESSOR

M.Tech, Ph.D

8

9

Dr. CHANDRA SHEKHAR NISHAD

ASSOCIATE PROFESSOR

M.Tech, Ph.D

7

10

Mr. U. RAVINDRAN

ASST PROFESSOR

M.Tech, Ph.D (Pursuing)

8

11

Mr. MOHAMED MAIDEENMOHAMED IQBAL

ASST PROFESSOR

M.Tech

6.7

12

Mr. L. KARTHIKEYAN

ASST PROFESSOR

M.Tech

4

13

Ms. D. GAYATHRI

ASST PROFESSOR

M.Tech, Ph.D (Pursuing)

9

14

Mr. JYOTHEESH KARNAM

ASST PROFESSOR

M.Tech

5

15

Ms. SIVAKUMAR MONICA

ASST PROFESSOR

M.Tech

4.8

16

Ms. S. SAVITHA

ASST PROFESSOR

M.Tech, Ph.D (Pursuing)

4

17

Ms. B. MEENA

ASST PROFESSOR

M.Tech

8.3

18

Mr. K. ANJANEYULU

ASST PROFESSOR

M.Tech

21

19

Mr. DEEPA VARAMPATI

ASST PROFESSOR

M.Tech

6.8

20

Mr. SASIREKHA B

ASST PROFESSOR

M.Tech

8.4

21

Mr. GURUMOHAN JERRY

ASST PROFESSOR

M.Tech

9

22

Dr. P. MOUNIKA

ASST PROFESSOR

M.Tech, Ph.D

12.7

23

Mr. JAYACHANDRAN JAYERAJ

ASST PROFESSOR

M.Tech

5

24

Mr. KARTHI E

ASST PROFESSOR

M.Tech

8

25

Ms. GANTAKOMALI

ASST PROFESSOR

M.Tech

7.6

26

Mr. VELUSAMY MAHESWARAN

ASST PROFESSOR

M.Tech

8.8

27

Mr. KARNAN S

ASST PROFESSOR

M.Tech

9.3

Journal Publications:

1 Dr. M. Lavanya, Associate Professor, Sri Venkateswara College and Technology, has published a paper entitled on “Advanced analytical framework for crop yield prediction leveraging diverse feature selection methods and machine learning classifiers in varied agricultural environments,” in Journal of emerging technologies and innovative research, ISSN 2349-5162, volume 11, issue 7, pp 158-163, 2024

2 Dr. K. Nandha Kumar, Associate Professor, Sri Venkateswara College and Technology, has published a paper entitled on “An Efficient Blockchain Assisted Electronic Health Record (HER) Authentication System” in Panamerican Mathematical Journal 35 No. 2. ISSN 1064, pp 57-71, 2025

3 Mr. George Sebastian, Associate Professor, Sri Venkateswara College and Technology, has published a paper entitled on “Circumvent the proliferation of counterfeited circulars using blockchain,” in International Journal of Critical Computer-Based Systems, 2024 Vol.11 No.3, pp.171 – 193, 2024.

Conference Publications:

1 1. Dr. M. Lavanya, Associate Professor, dept. of CSE(AI&ML), Sri Venkateswara College and Technology, has presented and published a paper on “Quantum Nanodevice Innovation for Smart Farming: Integrating Machine Learning and Blockchain Technology”. Proceedings of the 3rd International Conference on Optimization Techniques in the Field of Engineering (ICOFE-2024), Available at SSRN: https://ssrn.com/abstract=5083248 or http://dx.doi.org/10.2139/ssrn.5083248, November 15, 2024

2 Dr. M. Lavanya, Associate Professor, dept. of CSE(AI&ML), Sri Venkateswara College and Technology, has presented and published a paper on “Comparative analysis of ransomware attacks: Tactics, impact, and response” in the 1st International Conference on Digital Transformation & Sustainability of Business (ICDTSB), eISBN: 9781003606185, held on March 29th -30th ,2024.

3 M. Lavanya, Associate Professor, dept. of CSE(AI&ML), Sri Venkateswara College and Technology, has presented and published a paper on “Behavior digital forensic model as a digital forensics techniques for investigating cyber incidents” in the 1st International Conference on Digital Transformation & Sustainability of Business (ICDTSB), , eISBN: 9781003606185, held on March 29th -30th, 2024.

4 S. Savitha, Assistant Professor, dept. of CSE(AI&ML), Sri Venkateswara College and Technology, has presented and published a paper on “A Survey on text classification approaches based on NLP” in the 1st International Conference on Digital Transformation & Sustainability of Business (ICDTSB), eISBN: 9781003606185, held on March 29th-30th, 2024.

5 Dr. K. NandhaKumar, Associate Professor, dept. of CSE(AI&ML), Sri Venkateswara College and Technology, has presented and published a paper on entitled “Brain Tumor Division for MR Brain Images utilizing Artificial Bee Colony with Interval Type-II Fuzzy Strategies”, organized by Sambhram University, Jizzax, Uzbekistan on November 11th ,2024.

6 Dr. K. NandhaKumar, Associate Professor, dept. of CSE(AI&ML), Sri Venkateswara College and Technology, has presented and published a paper on a National Conference entitled “An optimization Data analytics on real time application using Machine Learning techniques”, organized by VEMU Institute of Technology, Andhra Pradesh, ISBN: 978-93-48512-15-4, during February 27th to 28th, 2025.

Book Chapter Publications:

1 1. M. Lavanya, Associate Professor, Sri Venkateswara College and Technology, has published a book chapter in Digital Transformation and Sustainability of Business, Taylor & Francis Group, CPR Press entitled “Comparative analysis of ransomware attacks: Tactics, impact, and response,” PP: 122|4 pages, ISBN9781003606185, 2025.

2 M. Lavanya, Associate Professor, Sri Venkateswara College and Technology, has published a book chapter in Digital Transformation and Sustainability of Business, Taylor & Francis Group, CPR Press entitled “Behavior digital forensic model as a digital forensics techniques for investigating cyber incidents” pp:119|4 pages, ISBN9781003606185, 2025.

3 3. S. Savitha, Assistant Professor, Sri Venkateswara College and Technology, has published a book chapter in Digital Transformation and Sustainability of Business, Taylor & Francis Group, CPR Press entitled “A Survey on text classification approaches based on NLP, PP: 80|4 pages ISBN9781003606185, 2025.

Journal Publications:

1 Dr. M. Lavanya, Associate Professor, Sri Venkateswara College and Technology, has published a paper entitled “Advanced analytical framework for crop yield prediction leveraging diverse feature selection methods and machine learning classifiers in varied agricultural environments,” in Journal of Emerging Technologies and Innovative Research, ISSN 2349-5162, Vol. 11, Issue 7, pp. 158–163, 2024.

2 Dr. R. Mohanraj, Associate Professor, Sri Venkateswara College and Technology, has published a paper entitled “Fuzzy Heuristics in Content Based Image Retrieval,” in International Journal of Engineering Research and Science & Technology, ISSN: 2319-5991, Vol. 21, Issue 1, pp. 171–178, 2025.

3 Dr. K. Nandha Kumar, Associate Professor, Sri Venkateswara College and Technology, has published a paper entitled “An Efficient Blockchain Assisted Electronic Health Record (EHR) Authentication System,” in Panamerican Mathematical Journal, Vol. 35, No. 2, ISSN 1064, pp. 57–71, 2025.

4 Dr. K. Nandha Kumar, Associate Professor, Sri Venkateswara College and Technology, has published a paper entitled “A Hybrid Particle Swarm Optimization and C4.5 for Network Intrusion Detection and Prevention System,” in International Journal of Computing, Vol. 23, Issue 1, ISSN 2312-5381, DOI: 10.47839/ijc.23.1.3442, pp. 109–115, 2024.

5 Mr. George Sebastian, Associate Professor, Sri Venkateswara College and Technology, has published a paper entitled “Circumvent the proliferation of counterfeited circulars using blockchain,” in International Journal of Critical Computer-Based Systems, Vol. 11, No. 3, pp. 171–193, 2024.

6 Dr. R. Mohanraj, Associate Professor, Sri Venkateswara College and Technology, has published a paper entitled “A real-time environmental air pollution predictor model using dense deep learning approach in IoT infrastructure,” in Global NEST Journal, Vol. 26, No. 3, pp. 1–16, 2024.

7 Dr. R. Mohanraj, Associate Professor, Sri Venkateswara College and Technology, has published a paper entitled “Securing the Digital Commerce Spectrum and Cyber Security Strategies for Web, E-commerce, M-commerce, and E-mail Security,” in Journal of Cybersecurity and Information Management (JCIM), Vol. 14, No. 01, pp. 34–49, 2024.

Conference Publications:

1 Dr. M. Lavanya presented “Quantum Nanodevice Innovation for Smart Farming: Integrating Machine Learning and Blockchain Technology” at the 3rd International Conference on Optimization Techniques in the Field of Engineering (ICOFE-2024), November 15, 2024. [SSRN | DOI]

2 Dr. M. Lavanya presented “Comparative analysis of ransomware attacks: Tactics, impact, and response” at the 1st International Conference on Digital Transformation & Sustainability of Business (ICDTSB), eISBN: 9781003606185, March 29–30, 2024.

3 Dr. R. Mohanraj presented “The artificial intelligence for NGO” at the 1st ICDTSB, eISBN: 9781003606185, March 29–30, 2024.

4 M. Lavanya presented “Behavior digital forensic model as a digital forensics techniques for investigating cyber incidents” at the 1st ICDTSB, eISBN: 9781003606185, March 29–30, 2024.

5 S. Savitha presented “A Survey on text classification approaches based on NLP” at the 1st ICDTSB, eISBN: 9781003606185, March 29–30, 2024.

6 Dr. R. Mohanraj presented “Fuzzy Heuristics in Content Based Image Retrieval” at the International Conference on Innovations and Recent Trends in Computer Science (ICIRTCS-24), St. Martin’s Engineering College, Hyderabad, December 17–18, 2024.

7 Dr. G. Kavitha presented “Food Calory Estimation and BMI Prediction Using ML” at the International Conference on Computational Intelligence, Emerging Technologies, and Smart Systems, RK University, Rajkot, March 28, 2025.

8 Dr. K. NandhaKumar presented “Brain Tumor Division for MR Brain Images utilizing Artificial Bee Colony with Interval Type-II Fuzzy Strategies” at Sambhram University, Jizzax, Uzbekistan, November 11, 2024.

9 Dr. K. NandhaKumar presented “An optimization Data analytics on real time application using Machine Learning techniques” at a National Conference organized by VEMU Institute of Technology, Andhra Pradesh, ISBN: 978-93-48512-15-4, February 27–28, 2025.

Book Chapter Publications:

1 Dr. R. Mohanraj, Associate Professor, Sri Venkateswara College and Technology, has published a book chapter in Digital Transformation and Sustainability of Business, Taylor & Francis Group, CPR Press entitled “The artificial intelligence for NGO,” PP: 92 | 4 pages, ISBN9781003606185, 2025.

2 M. Lavanya, Associate Professor, Sri Venkateswara College and Technology, has published a book chapter in Digital Transformation and Sustainability of Business, Taylor & Francis Group, CPR Press entitled “Comparative analysis of ransomware attacks: Tactics, impact, and response,” PP: 122 | 4 pages, ISBN9781003606185, 2025.

3 M. Lavanya, Associate Professor, Sri Venkateswara College and Technology, has published a book chapter in Digital Transformation and Sustainability of Business, Taylor & Francis Group, CPR Press entitled “Behavior digital forensic model as a digital forensics techniques for investigating cyber incidents,” PP: 119 | 4 pages, ISBN9781003606185, 2025.

4 S. Savitha, Assistant Professor, Sri Venkateswara College and Technology, has published a book chapter in Digital Transformation and Sustainability of Business, Taylor & Francis Group, CPR Press entitled “A Survey on text classification approaches based on NLP,” PP: 80 | 4 pages, ISBN9781003606185, 2025.

Data will be updated soon!

Data Analytics Using R Programming Laboratory

Objectives

  • Understand R Programming Basics – Learn the fundamentals of R, including syntax, data types, and basic operations.
  • Data Manipulation & Cleaning – Use packages like dplyr and tidyverse to manipulate and clean datasets.
  • Data Visualization – Develop skills in visualizing data using ggplot2, base R, and other libraries.
  • Statistical Analysis – Perform statistical operations such as hypothesis testing, regression, and correlation.
  • Machine Learning Basics – Implement basic machine learning algorithms like linear regression, decision trees, and clustering in R.
  • Time Series Analysis – Analyze time series data using packages like forecast and TSA.
  • Exploratory Data Analysis (EDA) – Learn to summarize and interpret datasets using various statistical techniques.
  • Big Data Handling – Work with large datasets using data.table, sparklyr, and other big data tools in R.
  • Text Mining & NLP – Perform basic text analysis using tm and tidytext packages.
  • Project Work – Apply concepts learned in a real-world dataset to derive insights.

Outcomes

Upon successful completion of this lab, students will be able to:

  • Write and execute R scripts for data analysis.
  • Perform data cleaning, transformation, and wrangling efficiently.
  • Create meaningful visualizations to interpret data.
  • Apply statistical techniques to draw inferences from datasets.
  • Implement predictive modeling using machine learning algorithms.
  • Work with real-world datasets and conduct exploratory data analysis (EDA).
  • Handle time series data and perform forecasting.
  • Utilize R for text analytics and natural language processing.
  • Optimize performance when working with large datasets.
  • Develop a mini-project using R for data analytics applications.

DEEP LEARNING Laboratory

Objectives

  • Understand Neural Networks – Learn the fundamentals of artificial neural networks (ANNs), perceptrons, and activation functions.
  • Implement Deep Learning Models – Develop and train deep learning models using frameworks like TensorFlow and PyTorch.
  • Work with Different Architectures – Explore different deep learning architectures such as CNNs, RNNs, LSTMs, and GANs.
  • Data Preprocessing for Deep Learning – Perform data augmentation, normalization, and handling of large-scale datasets.
  • Model Training and Optimization – Learn techniques for optimizing deep learning models, including loss functions, gradient descent, and backpropagation.
  • Hyperparameter Tuning – Experiment with hyperparameters like learning rate, batch size, and number of layers for better model performance.
  • Image Classification & Object Detection – Implement and evaluate models for computer vision tasks using convolutional neural networks (CNNs).
  • Natural Language Processing (NLP) – Apply deep learning models for text-based tasks such as sentiment analysis, text generation, and machine translation.
  • Reinforcement Learning Basics – Understand and implement reinforcement learning algorithms.
  • Deploy Deep Learning Models – Learn to deploy trained models using APIs, cloud platforms, or embedded systems.

Outcomes

Upon successful completion of this lab, students will be able to:

  • Implement Basic Neural Networks – Design and implement simple feedforward and deep neural networks.
  • Work with Deep Learning Frameworks – Utilize TensorFlow, Keras, and PyTorch for developing deep learning models.
  • Preprocess and Handle Large Datasets – Perform data cleaning, augmentation, and normalization techniques for deep learning applications.
  • Train and Optimize Deep Learning Models – Apply optimization techniques like gradient descent, backpropagation, and batch normalization to improve model performance.
  • Develop Computer Vision Applications – Build and train Convolutional Neural Networks (CNNs) for tasks like image classification and object detection.
  • Apply Deep Learning in NLP – Use Recurrent Neural Networks (RNNs), LSTMs, and Transformers for tasks such as text classification and sentiment analysis.
  • Experiment with Advanced Architectures – Implement and evaluate deep learning architectures like GANs, Autoencoders, and Transfer Learning models.
  • Fine-tune Hyperparameters – Optimize deep learning models using techniques like learning rate tuning, dropout, and early stopping.
  • Deploy Deep Learning Models – Deploy trained models in real-world applications using cloud platforms, APIs, or embedded systems.
  • Work on Real-World Projects – Apply deep learning concepts to solve practical problems in domains like healthcare, finance, and autonomous systems.

C Programming and Data Structures Laboratory

Objectives

  • Understand the Basics of C Programming – Learn fundamental concepts such as data types, operators, control structures, and functions.
  • Develop Problem-Solving Skills – Implement algorithms and logic to solve computational problems using C.
  • Work with Arrays and Pointers – Gain proficiency in handling arrays, pointers, and dynamic memory allocation.
  • Implement String Manipulation Techniques – Perform operations such as searching, sorting, and pattern matching on strings.
  • Explore Data Structures – Implement and analyze fundamental data structures such as stacks, queues, linked lists, trees, and graphs.
  • Understand Recursion and Iteration – Apply recursive and iterative approaches to problem-solving.
  • Perform File Handling Operations – Read from and write to files using C for data processing.
  • Analyze Algorithm Efficiency – Understand time and space complexity for different data structures and algorithms.
  • Implement Sorting and Searching Algorithms – Apply algorithms like Bubble Sort, Quick Sort, Merge Sort, and Binary Search.
  • Develop Real-World Applications – Build small applications using C and data structures to reinforce learning.

Outcomes

Upon successful completion of this lab, students will be able to:

  • Write Efficient C Programs – Develop structured and optimized code using C programming principles.
  • Apply Logical Thinking for Problem-Solving – Use programming constructs effectively to solve real-world problems.
  • Manipulate Arrays and Pointers – Perform efficient data processing using arrays, pointers, and memory management.
  • Implement and Analyze Data Structures – Construct and evaluate different data structures for various applications.
  • Use Recursion Effectively – Solve problems using recursive functions where applicable.
  • Perform File Handling Operations – Read, write, and manipulate data stored in files using C.
  • Optimize Code for Performance – Evaluate and improve the efficiency of algorithms using complexity analysis.
  • Design and Implement Sorting and Searching Algorithms – Apply different sorting and searching techniques based on problem requirements.
  • Develop Small-Scale Projects – Create real-world applications integrating C programming with data structures.
  • Gain Practical Experience for Further Studies – Establish a strong foundation in C and data structures, which is essential for advanced programming and software development.

Problem Solving and Programming Using Python Lab Laboratory

Objectives

  • Understand Python Basics – Learn fundamental concepts such as syntax, variables, data types, and operators.
  • Develop Problem-Solving Skills – Use Python to implement logic-based solutions for computational problems.
  • Work with Control Structures – Utilize conditional statements, loops, and functions for efficient programming.
  • Manipulate Data Structures – Implement lists, tuples, sets, and dictionaries for data storage and processing.
  • Apply Functions and Modules – Write modular programs using functions, lambda expressions, and built-in libraries.
  • Perform File Handling Operations – Read from and write to files for data storage and retrieval.
  • Work with Object-Oriented Programming (OOP) – Implement classes and objects to solve real-world problems.
  • Use Exception Handling – Write robust programs by managing errors and exceptions effectively.
  • Explore Libraries for Data Analysis – Utilize NumPy, Pandas, and Matplotlib for basic data processing and visualization.
  • Develop Small-Scale Applications – Build mini-projects to reinforce programming concepts.

Outcomes

Upon successful completion of this lab, students will be able to:

  • Write and Execute Python Programs – Develop structured and efficient Python code.
  • Implement Logical Solutions to Problems – Apply problem-solving strategies using Python.
  • Use Control Structures Effectively – Implement loops, conditionals, and functions for program flow control.
  • Handle Data Structures Efficiently – Store and manipulate data using lists, tuples, dictionaries, and sets.
  • Develop Modular and Reusable Code – Use functions and modules to improve code reusability and maintainability.
  • Perform File Operations – Read, write, and manipulate files for data processing applications.
  • Apply Object-Oriented Programming Concepts – Design and implement programs using OOP principles.
  • Handle Exceptions Gracefully – Use exception handling mechanisms to write error-free programs.
  • Utilize Python Libraries for Data Analysis – Work with NumPy, Pandas, and Matplotlib for basic data science tasks.
  • Create Real-World Applications – Develop Python-based mini-projects demonstrating problem-solving skills.

Artificial Intelligence and Machine Learning Laboratory

Objectives

  • Understand AI and ML Fundamentals – Learn the core concepts of artificial intelligence and machine learning.
  • Implement Basic AI Algorithms – Develop AI techniques such as search algorithms, game playing, and knowledge representation.
  • Work with Machine Learning Models – Implement supervised and unsupervised learning models using Python libraries like Scikit-Learn and TensorFlow.
  • Perform Data Preprocessing – Clean, transform, and prepare datasets for training ML models.
  • Train and Evaluate ML Models – Use metrics such as accuracy, precision, recall, and F1-score to assess model performance.
  • Apply Feature Engineering Techniques – Optimize datasets by selecting and transforming features for better model performance.
  • Explore Neural Networks and Deep Learning – Understand and implement artificial neural networks (ANNs) and deep learning models using TensorFlow/Keras.
  • Develop AI-Based Solutions – Solve real-world problems using AI techniques like NLP, computer vision, and reinforcement learning.
  • Work with Big Data and Cloud AI Tools – Use cloud platforms and big data tools for scalable AI and ML applications.
  • Deploy ML Models – Learn to deploy trained ML models as web applications or APIs for practical use.

Outcomes

Upon successful completion of this lab, students will be able to:

  • Implement AI Search Algorithms – Develop AI solutions using informed and uninformed search techniques.
  • Build and Evaluate Machine Learning Models – Apply classification, regression, clustering, and dimensionality reduction techniques.
  • Handle and Preprocess Large Datasets – Clean and prepare raw data for effective ML model training.
  • Apply Supervised and Unsupervised Learning – Implement models like decision trees, SVMs, k-means clustering, and principal component analysis (PCA).
  • Develop Neural Networks for Deep Learning – Train and optimize deep learning models using frameworks like TensorFlow and PyTorch.
  • Analyze Model Performance – Use performance evaluation metrics to compare and improve ML models.
  • Implement Natural Language Processing (NLP) – Apply AI for text processing tasks such as sentiment analysis and text classification.
  • Work with Computer Vision Techniques – Implement image classification and object detection using CNNs.
  • Deploy ML Models in Real-World Applications – Host and integrate trained models into web or mobile applications.
  • Build AI and ML-Based Projects – Solve domain-specific problems using AI and ML methodologies.

Syllabus Downloads


B.Tech R23 CSE(AIML) Detailed Syllabus

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B.Tech R20 CSE(AIML) Detailed Syllabus

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Department Association

INCREDIBLE INTELLIGENCE DEPARTMENT ASSOCIATION TEAM (IIDA)

S.No. Name Designation

1

Mr. L. Karthikeyan

FACULTY ADVISOR

2

Mr. G. Akash

21781A3349

PRESIDENT

3

Mr. J. Venkata Subbaiah

22781A3343

VICE PRESIDENT

4

Mr. C. Sharan Sai

23781A3326

SECRETARY

5

Mr. A. Charan

23781A3102

JOINT SECRETARY

6

Mr. Gokul Sai

23785A3104

STUDENT TREASURER

7

Mr. S. Jamshid

21781A33E2

STUDENT EVENT ORGANIZER

8

Mr. Naveen

22781A33

STUDENT EVENT ORGANIZER

9

Mr. Muneeswar

22781A33

STUDENT EVENT ORGANIZER

10

Mr. V. S. Revanth(CSM)

23781A33G7

STUDENT ORGANIZER

11

Ms. T. Vedhavathi

23781A33G0

STUDENT ORGANIZER

12

Ms. P. Nagathanusha

23781A3139

STUDENT ORGANIZER

13

Ms. K. Deepika

23781A3373

STUDENT ORGANIZER

14

Ms. C. Sneha

22781A3332

STUDENT ORGANIZER

15

Ms. T. Charitha Reddy

22781A33D1

STUDENT ORGANIZER

16

Ms. N. Jaya Lakshmi

22781A3128

STUDENT ORGANIZER

Data will be updated soon!

Data will be updated soon!

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Contact Details

faculty photo

Dr. M. Lavanya

HOD & ASSOCIATE PROFESSOR

MCA, M.Tech, Ph.D

hodcse@svcetedu.org

Data will be updated soon!

Data will be updated soon!

One Day National Conference In Hybrid Mode On "Emerging Trends In Material Science Unveiling New Materials And Sustainability " On 29th April, 2025 By Department Of HAS, SVCET.

5 Day Faculty Development Programme On Modern Cybersecurity With AI ML 27-03-2025 To 31-03-2025 Organized By Department Of MCA

Personal Verification Of Answer Scripts Notification

Revaluation Notification

Announcements

One Day National Conference In Hybrid Mode On "Emerging Trends In Material Science Unveiling New Materials And Sustainability " On 29th April, 2025 By Department Of HAS, SVCET.

5 Day Faculty Development Programme On Modern Cybersecurity With AI ML 27-03-2025 To 31-03-2025 Organized By Department Of MCA

Personal Verification Of Answer Scripts Notification

Revaluation Notification

About Us

Academics

Placements

Important Links

Contact Details

7729999158 principal@svcetedu.org info@svcetedu.org R.V.S Nagar, Tirupathi Road, Chittoor,Andhra Pradesh - 517127.