6 days International eFDP on Applications of Machine Learning in IoT and Data Analytics

Date: 5th June 2023 to 14 June 2023

Venue: Virtual

Submitted by :Dr. Madhuri Rao, Assistant Professor at SICSR

The 6 days International e-FDP on “Applications of Machine Learning in Internet of Things and Data Analytics” was held from 19th June 2023 to 26th June 2023. It comprised of 12 sessions that were delivered by eminent speakers from premier institute of national and international repute. More than 90 Participants registered for the program. The participants gained knowledge on how real world problems could be addressed using Machine Learning and Deep Learning approaches. Latest tools such as Orange, TinyML, R were discussed and their efficient use was demonstrated by the speakers. Open Research oriented problems that need to be addressed in Internet of Things and data analytics with the help of ML and transfer learning were discussed. Various ML algorithms such regression, SVM, KNN, Naïve Bayes, Time Series Forecasting, etc and their implementation in solving complex real life problems were also explained briefly. The titles of the talks session wise are –

  1. Session 1- Data Analytics to Machine Learning
  2. Session 2- Machine learning in Blockchain based Smart City
  3. Session 3- Application of machine learning/Deep learning in computational biology
  4. Session 4- Introduction to Predictive Coding
  5. Session 5- Applied Machine Learning through Visual Programming
  6. Session 6- IoT and AI/ML based Intelligent Systems for Real-Time Structural Applications in Civil Engineering
  7. Session 7- Application of Deep Transfer Learning for Skin Lesion Detection.
  8. Session 8- Harnessing the Power of IoT and TinyML for Intelligent Edge Computing and Real-time Decision making
  9. Session 9- Applications of Machine Learning in Cyber Security Data Analysis
  10. Session 10- Deep Learning for Disease Classification
  11. Session 11- Addressing Challenges in Digital Image Processing with machine Learning techniques
  12. Session 12- Machine Learning for Real-World Problems



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