Resources

Laboratories : The department has all types of modern teaching- learning resources in the department.

  • The department is equipped with state-of-the-art laboratories, providing students with the necessary resources for practical learning and specialized software tools.
  • The technical staff is appointed by the institute and allotted by the Head of the department considering their skill set, by issuing formal order letters.
  • Adequate technical manpower ensures smooth laboratory operations, technical support, and assistance to students and faculty for academic and research activities. The list of laboratories and technical manpower is mentioned in Table
  • As and when any facility is created at the department in the form of hardware or software, the support staff is given training and certification to ensure their scaling up. Refer image below
Name of LabDetails
AI Nexus Laboratory
 

Skills Developed

  • Implementing classic AI search and optimization algorithms
  • Building rule-based and logic-based reasoning systems
  • Working with NLP tools for text/language-based applications
  • Applying computer vision techniques for image-based tasks
  • Designing intelligent agents that make autonomous decisions

Learning Outcomes

  • Understanding how different AI subfields (search, NLP, vision, reasoning) connect and complement each other.
  • Ability to design and implement AI-based problem-solving systems
  • Exposure to real-world AI applications (chatbots, recommendation systems, game AI)
  • Strengthens foundation for advanced AI/ML specializations

Additional Benefits

  • Encourages interdisciplinary thinking by connecting AI with linguistics, vision, and logic
  • Builds a diverse project portfolio spanning multiple AI domains
  • Prepares students for AI research, robotics, and applied AI industry roles

Hardware

HP- i7 13th Gen, RAM-16GB, SSD-1.5TB, Monitor-23.8”.

HP Pro Desk 4Gli Tower, INTL CU7265, RAM- 16 GB
SSD- 512 GB, Graphics- NVIDIA 8 GB
Monitor-24” IPS

Software

Operating System – Windows 11Pro, Linux,
Microsoft Office 16
Google Chrome, Microsoft Edge
Other Software- Adobe Reader, VS Code, Python3

Machine Intelligence Laboratory

Skills Developed

  • Coding proficiency and familiarity with ML libraries (NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch)
  • Handling real-world messy data (missing values, outliers, imbalanced classes)
  • Model tuning — adjusting hyper parameters to improve accuracy
  • Analytical and problem-solving skills through trial-and-error experimentation
  • Writing clean, reproducible, and well-documented code

Learning Outcomes

  • Understanding the complete ML pipeline — from data collection to model deployment
  • Ability to experiment with and compare different algorithms on the same dataset
  • Exposure to cloud platforms or GPU-based training for larger models
  • Bridges theoretical concepts with practical, real-world application

Additional Benefits

  • Encourages teamwork through group assignments or mini-projects
  • Helps build a portfolio of mini-projects for resumes or interviews
  • Prepares students for research projects, internships, and industry-level ML tasks.

Hardware

HP Pro Desk 4Gli Tower, INTL CU7265, RAM- 16 GB
SSD- 512 GB, Graphics- NVIDIA 8GB, Monitor-24” IPS

Software

Operating System – Windows 11Pro, Linux,
Microsoft Office 16
Google Chrome, Microsoft Edge
Other Software- Adobe Reader, VS Code, Python3

Lab Photos

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