The Department of Computer Science and Engineering (AI & ML) is equipped with advanced and industry-aligned laboratories that provide students with hands-on experience, practical skill development, and exposure to real-time software and hardware environments.

Each lab is designed to support project-based learning, experimentation, and research in core and emerging areas of computing and artificial intelligence.

The department hosts the following laboratories:

Natural Language Processing (NLP) Lab

The Natural Language Processing Lab is dedicated to the study of human-computer interaction using natural languages. Students gain hands-on expertise in tokenization, parsing, sentiment analysis, machine translation, and text summarization using toolkits like NLTK and SpaCy. Projects focus on building conversational AI agents, language models, and systems that extract meaningful information from large volumes of text data.

Generative AI Lab

The Generative AI Lab focuses on the cutting edge of AI, training students to work with Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Large Language Models (LLMs). Students explore techniques for creating novel content, including synthetic image generation, deepfakes, realistic text creation, and music composition. This lab prepares innovators for roles in creative technology and advanced AI research.

Soft Computing Lab

The Soft Computing Lab emphasizes computational techniques inspired by biological and natural systems, such as Fuzzy Logic, Genetic Algorithms, Swarm Optimization, and Neural Networks. Students learn how these techniques are applied in real-world optimization, classification, and prediction problems.

Expert Systems Lab

The Expert Systems Lab provides training in developing knowledge-based systems and decision-support tools. Students learn about rule-based systems, inference engines, knowledge representation (ontologies), and reasoning under uncertainty. The focus is on creating intelligent systems that can mimic the decision-making ability of a human expert in specific domains, such as medical diagnostics, financial forecasting, or complex problem-solving.

Machine Learning Lab

The Machine Learning Lab provides hands-on training in supervised and unsupervised learning algorithms, model training, evaluation, and data preprocessing using Python, Scikit-Learn, and real-world datasets. Students build ML models that solve real-time prediction and classification problems across various domains.

Deep Learning Lab

This specialized lab trains students in Deep Neural Networks, Convolutional Neural Networks (CNNs), RNNs, NLP models, and image/video processing using frameworks such as TensorFlow and PyTorch. Students work on cutting-edge applications such as image classification, face recognition, medical imaging, and speech processing.

DevOps Lab

The DevOps Lab focuses on automation, deployment pipelines, and system orchestration using Docker, Kubernetes, Jenkins, Git, and CI/CD workflows. Students learn how to deploy, monitor, and update AI/ML applications efficiently, preparing them for cloud and application delivery roles.

MongoDB Lab

The MongoDB Lab provides specialized training in modern Data Warehousing and NoSQL databases, with a practical focus on systems like MongoDB. Students delve into the management, scaling, and optimization of massive, unstructured datasets. This expertise is crucial for roles in Big Data architecture and backend development, enabling students to design and maintain scalable data infrastructure required by contemporary web and AI services.