IIT Kharagpur Offers 12-Week Free Online Course on Deep Learning

IIT Kharagpur has invited applications from interested candidates for a 12 week free online course called Deep Learning For Visual Computing. The course, which will be conducted from 23 January to 14 April 2023, will cover both theory and coding practice of deep learning for visual computing through curated exercises with Python and PyTorch on current developments. The course will be conducted through the SWAYAM NPTEL platform and also carries 3 credit points. 

The course will be conducted by Debdoot Sheet who  is an assistant professor of electrical engineering at IIT Kharagpur. The professor has gained his MS and PhD degrees in computational medical imaging and machine learning from the Indian Institute of Technology Kharagpur in 2010 and 2014, respectively. He is also a member of IEEE, SPIE, ACM, IUPRAI and BMESI and serves as an Editor of IEEE Pulse since 2014.

Important Facts About the IIT Kharagpur Free Online Course

Participants who wish to take the course are advised to keep in mind the following important details:

• The course is being offered on the SWAYAM NPTEL platform.
• While the course is free to take, those who wish to take the e-certificate will have to pay Rs 1000 and take an examination that will be conducted in person on 30 April 2023 at NPTEL designated test centres.
• The certificate will be provided based on the average of best 8 assignments out of the total 12 assignments to be given in the course and performance of the candidate in the final exam.
• Final score will be based on 25 percent of the assignment and 75 percent of the examination score.
• The certificate will have the name of the candidates along with their photo and final marks scored.
• The e-certificate will also carry logos of NPTEL and IIT Kharagpur.
• Hard copies of the certificates will not be dispatched.

Who Can Apply for the IIT Kharagpur Free Online Course?

The course is open to any interested professional or student. However, it would be most beneficial for students pursuing the following degrees:

• Electrical engineering.
• Electronics and communications engineering.
• Computer science engineering.

Participants will need to have a background in digital image processing, and machine learning.

List of Topics to be Covered

Participants will be introduced to the theory and coding practice of deep learning through the following topics:

• Introduction to visual computing and neural networks.
• Multilayer perceptron to deep neural networks with autoencoders.
• Autoencoders for representation learning and MLP initialization.
• Stacked, sparse, denoising autoencoders and ladder training.
• Cost functions, learning rate dynamics and optimization.

Those who wish to know more are advised to visit the official website for further information.