Teaching Labs

The Department Of Computer Science and Engineering is establishing Teaching Labs (upcoming) in Three Verticals each with a seating capacity of  60

Hardware Lab

This is a hardware driven lab primarily comprising of

  •   Low end computing devices
  •   Boards
  •   Embedded Devices
  •   Kits
  •   Software Tools, etc.

Networks & Systems Lab

The characteristics of this lab is as follows:

  •   Simple front-end computers
  •   Single board computers
  •   Router Boards
  •   USRP Boards
  •   Switches
  •   Stand-alone cloud setup
  •   Isolated Network setup, etc.

Data Science & Analytics Lab

This is a lab having high-end GPU enabled computers primarily used for running Machine Learning and Analytical jobs


Proposed Courses in Teaching Labs

Each Lab is equipped with different types of systems fine tuned for teaching different types of courses.

Hardware Lab

  1. Digital Systems
  2. Computer Organization
  3. Computer System Design
  4. Computer System Architecture
  5. Processor Design
  6. System-on-Chip Design
  7. Hardware-software Co-design
  8. Hardware Accelerator Design
  9. Embedded System Design
  10. Internet of Things
  11. GPU Computing
  12. VLSI Design and Verification

Networks & Systems Lab

  1. Data Structures and Algorithms
  2. Computer Networks
  3. Advanced Computer Networks
  4. Stochastic Network Optimization
  5. Software Engineering
  6. Cloud Computing
  7. Parallel Computing
  8. Parallel Numerical Linear Algebra
  9. Database Systems
  10. Distributed Systems
  11. Operating Systems
  12. Compiler Design
  13. Introduction to Programming
  14. Programming Methodology
  15. Performance Evaluation of Computer Systems
  16. Advanced Data Structures and Algorithms
  17. Industrial software Engineering
  18. Network Security
  19. Cyber Security

Data Science & Analytics Lab

  1. Machine Learning
  2. Artificial Intelligence
  3. Deep Learning
  4. Reinforcement Learning
  5. Artificial Neural Networks
  6. Predictive Data Modelling
  7. Industrial Data Science and Engineering
  8. Data Science for Software Engineering
  9. Speech Processing
  10. Computer Graphics
  11. Computer Vision
  12. Natural Language Processing
  13. Computational Methods in Optimization