Available courses

  • To build new technology, knowledge system based on innovations and can address local challenges.
  • Creating environment to innovate and build products towards sustainable development goals.
  • To provide platform for speedy communication and market reach of technology/ product developed by students.

This course provides practical, foundation level training that enables immediate and effective participation in Big Data and other Analytics projects.

Data science is a field that deals with unstructured, structured data, and semi-structured data. It involves practices like data cleansing, data preparation, data analysis, and much more.

Data science is the combination of: statistics, mathematics, programming, and problem-solving;, capturing data in ingenious ways; the ability to look at things differently; and the activity of cleansing, preparing, and aligning data. This umbrella term includes various techniques that are used when extracting insights and information from data.

Big data refers to significant volumes of data that cannot be processed effectively with the traditional applications that are currently used.

is the science of examining raw data to reach certain conclusions.

Data analytics involves applying an algorithmic or mechanical process to derive insights and running through several data sets to look for meaningful correlations.

  • The Deep Learning Course will help you to understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.

    Course begin by learning the fundamentals of deep learning. Then examine the foundational algorithms underpinning modern deep learning: gradient descent and backpropagation. Once those foundations are established, explore design constructs of neural networks and the impact of these design decisions. Finally, the course explores how neural network training can be optimized for accuracy and robustness. 

    The Deep Learning provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.

Course Outcomes: 

 1)        To demonstrate the design knowledge of Ubiquitous Computing and its

             applications. 

 2)        To analyze and explore smart devices and services used in Ubiquitous

             Computing.

 3)        Identify the significance of actuators and controllers in real time

             application design.

 4)        Apply and use the concept of HCI to comprehend the design of

             automation applications.

5)        Evaluating Ubiquitous Computing privacy and identifying the challenges

            associated with Ubiquitous Computing privacy.

 6)        Recognize knowledge of ubiquitous and service oriented networks along

             with Ubiquitous Computing management.

1. To understand the basic concepts of machine learning and apply them for the various problems.
2. To learn various machine learning types and use it for the various machine learning tasks.
3. To optimize the machine learning model and generalize it.

1) The course aims to provide an understanding of the principles on which the distributed systems          are based; their architecture, algorithms and how they meet the demands of Distributed                      applications.


2) The course covers the building blocks for a study related to the design and the implementation of     distributed systems and applications.

Course Outcomes: 

1)        To demonstrate the design knowledge of Ubiquitous Computing and its applications.

 

2)        To analyze and explore smart devices and services used in Ubiquitous Computing.

 

3)        Identify the significance of actuators and controllers in real time application design.

 

4)        Apply and use the concept of HCI to comprehend the design of automation applications.

 

5)        Evaluating Ubiquitous Computing privacy and identifying the challenges associated with Ubiquitous Computing privacy.

 

6)        Recognize knowledge of ubiquitous and service oriented networks along with Ubiquitous Computing management.

    • This Course introduces students to computer networks and concentrates on building a firm foundation for understanding Data Communications and Computer Networks.
    • It is based around the OSI Reference Model that deals with the major issues in the bottom three (Physical, Data Link and Network) layers of the model.
    • The course provides the student with fundamental knowledge of the various aspects of computer networking and enables students to appreciate recent developments in the area.

Course Objective:

  •  Understanding Human learning aspects.
  •  Understanding primitives and methods in learning process by computer.
  •  Understanding of the fundamental issues and challenges of   machine   learning: data, model selection, model complexity, etc.
  •  Have an understanding of the strengths and weaknesses of many popular   machine learning approaches.
  •  Be able to design and implement various machine learning algorithms in a   range of real-world applications.

After studying this subject student should be able to

1. Develop algorithms for solving problems by using modular programming concepts

2. Abstract data and entities from the problem domain, build object models and design software solutions using object-oriented principles and strategies

3. Discover, explore and apply tools and best practices in object-oriented programming.

4. Develop programs that appropriately utilize key object-oriented concepts