Machine Learning for Computer Vision - Online Mode

Participants Information

Participants: 48
Speakers:10
Female participants:24
Male participants:24
Participants from SC/ST category:0
Research Scholars:05

Lab Sessions

  • Introduction to Python, Jupyter Notebook, Pytorch, Google Colab
  • Code writing for Image processing methods - Edge detection, and Image Enhancement
  • Neural Networks and Back Propagation
  • Python libraries, First program on Image classification using an ML algorithm
  • Building a classification model using CNN, Use of Tensorflow and Pytorch
  • Transfer learning and classification for plant disease classification
  • : Using YOLO the and Darknet Framework for object detection
  • Image Denoising with an autoencoder
  • Building a CNN model for image segmentation with application to fire/smoke segmentation in satellite images

Topics Covered

  • Frontiers of AI for Computer Vision
  • Intro to Image Processing, Algorithms on edge detection, image enhancement, segmentation etc
  • Intro to AI and ML (supervised and unsupervised learning methods. K-means, SVM) with applications to Image Processing
  • Neural Networks: Regularization, Optimization and Challenges in training deep neural networks, Vanishing gradient problem.
  • Convolution Neural Networks (CNNs)
  • Deep CNN architectures, Transfer Learning and Applications
  • Object Detection using CNN
  • Autoencoder and Their Applications
  • CNN Applications in Segmentation and Medical Image Analysis
  • Recent Trends in ML for CV

Highlights

List of Speakers

Prof. P.K. Biswas, IIT Kharagpur


Dr Rajib Jha, IIT Patna


Dr M Tanveer, IIT Indore


Dr. Partha Pratim Roy, IIT Roorkee


Prof. Aparajita Ojha, IIITDM Jabalpur


Dr Santosh Viparthi, IIT Guwahati /MNIT Jaipur


Prof. R. Balasubramanian, IIT Roorkee


Ms. Poornima Singh Thakur, IIITDM Jabalpur


Mr Samir Jain, IIITDM Jabalpur


Ms Shubhangi Chaturvedi, IIITDM Jabalpur




Feedback Summary

  • FDP was very informative and qualitative.

  • The program was organized perfectly.

  • Suggestions from Participants

  • Participants requested to conduct more FDPs of this type.

  • Participants requested to conduct more Hands on Sessions.