LABORATORIES

The Artificial Intelligence and Data Science department has following well-equipped laboratories with latest configurations of Workstations and computer systems.

Data Analytics Laboratory

Data Analytics lab is well-equipped with High end workstations with GPU processors with the configuration of Core-i7 systems and Graphics cards for conducting Data science lab with large data set, Data structures lab and DBMS lab.

The lab should help students:

    • Understand the data lifecycle: collection → cleaning → analysis → visualization

    • Gain hands-on experience with modern tools

    • Learn to work on individual and group projects

    • Explore real-world datasets

Data analytics tools like Python, R, PowerBI, Tableau, SQLite or PostgreSQL, Google Looker Studio, JupyterHub are used in this lab. The students perform various experiments in statistical analysis, mathematical models, statistics, data processing, data prediction, decision making, animated and interactive visualizations. Students can work with large data sets and visualize the statistical models and Data analysis.

Artificial Intelligence Laboratory

AI lab provides an environment where students can learn and experiment with core AI concepts, from classical search algorithms to modern machine learning and deep learning frameworks.

Objectives of the AI Lab

    • Understand foundational AI topics (e.g., search, logic, planning)

    • Implement algorithms from scratch

    • Apply machine learning and deep learning to real-world problems

    • Use AI frameworks and cloud tools

    • Encourage research, experimentation, and project development

Software & Tools in AI Lab
1. Programming & IDEs
    • Python: The primary language (due to rich AI ecosystem)
    • JupyterLab or Google Colab: For interactive coding
    • VS Code or PyCharm Edu: For structured development     
2. AI & ML Libraries
  • Classical AI:
    • AIMA-Python: Code for algorithms in “Artificial Intelligence: A Modern Approach”
    • Custom implementations of DFS, BFS, A*, minimax, alpha-beta
  • Machine Learning:
    • Scikit-learn: Classical ML
    • XGBoost, LightGBM: Ensemble methods
  • Deep Learning:
    • TensorFlow 2.x or PyTorch: Core DL frameworks
    •  Keras: High-level API for fast prototyping
    • spaCy, NLTK, Hugging Face Transformers
  • NLP & Vision:
    • spaCy, NLTK, Hugging Face Transformers
    • OpenCV, YOLO, Detectron2

3. Experimentation & Tracking

  • MLflow: Track experiments

  • Weights & Biases or Tensor Board: Visualize training progress

Deep Learning Laboratory

DL lab involves equipping students with both theoretical understanding and hands-on skills using modern tools and frameworks.

Deep Learning Lab Objectives
    • Understand the fundamentals of neural networks and backpropagation
    • Implement core deep learning architectures (CNNs, RNNs, Transformers)
    • Work with real-world datasets (images, text, audio, video)
    • Use modern frameworks like PyTorch and TensorFlow
    • Apply techniques like fine-tuning, transfer learning, and optimization
     
     

Essential Software & Tools for DL Lab

1. Programming Environment

  • Language: Python

  • IDEs/Notebooks:

  • JupyterLab

  • Google Colab (recommended for GPU access)

  • VS Code with Python extensions

2. Deep Learning Frameworks

  • TensorFlow 2.x + Keras – User-friendly and widely used

  • PyTorch – Preferred for research and flexibility

  • Optional: JAX – For high-performance gradient-based computation

3. Support Libraries

  • NumPy, Pandas, Scikit-learn – Preprocessing

  • OpenCV, PIL – Image handling

  • Matplotlib, Seaborn – Visualization

  • Hugging Face Transformers – Pretrained NLP models

  • Torchvision / TensorFlow Datasets – Dataset access

4. Experiment Tracking & Visualization

  • TensorBoard

  • Weights & Biases (WandB)

  • MLflow

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