The Department of Computer Science and Engineering, National Institute of Technology (NIT), Delhi is organizing an AICTE Training and Learning (ATAL) Academy Sponsored Online FDP on Machine Learning Frontiers in Healthcare from July 26 to 30, 2021.
This Faculty Development Programme (FDP) will provide a platform to present and discuss the techniques and the latest developments in the area of ISF with their applications in healthcare.
National Institute of Technology, Delhi is a public technical university located in Delhi, India. It has been declared as an Institute of National Importance by an act of the Parliament of India. It is one of the 31 National Institutes of Technology in India.
- This course is designed to provide an exposure to the fundamentals of Machine Learning Techniques for Healthcare analysis
- To understand the ability of a computer to learn a specific task from data or experimental observation for healthcare domain
- To provide up-to-date technologies in the machine Learning field for gait analysis.
The program is focused to discuss various aspects of computational intelligence. Following are the topics to be covered in this program:
- Health Care Informatics
- Grid and Cloud Computing in Healthcare
- Deep Learning in Medical Applications
- Blockchain and Healthcare
- Mobile Computing Systems in Healthcare
- IoT integration with ML in HealthCare
- Streaming Data Analytics in Health
- Distributed Computing in healthcare
- Standards for Semantics in Healthcare
- Reinforcement Learning for the pose estimation
- Hands-on: R/ Python/ MATLAB
Who can Attend?
The program is open to all faculty members, practitioners, and researchers; PG/ Doctoral Students for AICTE affiliated institutes. A maximum of 200 participants will be allowed as per AICTE norms. So please register soon. No TA/ DA will be paid to the participants.
Eminent academicians from IITs, NIT’s, and Industry experts will deliver the sessions with practical demonstrations.
To register for the FDP, click here.
The certificates shall be issued to those participants who have attended the program with a minimum of 80% attendance and scored a minimum of 60% marks on the test.
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