Hands-on Machine Learning with Python

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Hands-on Machine Learning with Python


We invite you to a three-day hands-on machine learning workshop organized by the IEEE student branch at INRS-EMT and IEEE Montreal section. We virtually host Dr. Pedram Abdolghader, a research and development scientist at Few-Cycle Inc., to interactively learn about machine learning with Python. Basic knowledge of programming and familiarity with Python language is required.



  Date and Time

  Location

  Hosts

  Registration



  • Start time: 14 Sep 2022 05:00 PM
  • End time: 16 Sep 2022 07:00 PM
  • All times are (UTC-04:00) Atlantic Time (Canada)
  • Add_To_Calendar_icon Add Event to Calendar
  • Starts 24 July 2022 08:00 AM
  • Ends 14 September 2022 08:00 AM
  • All times are (UTC-04:00) Atlantic Time (Canada)
  • No Admission Charge


  Speakers

Dr. Pedram Abdolghader Dr. Pedram Abdolghader

Biography:

Dr. Pedram Abdolghader finished his Ph.D. in atomic molecular and optical physics at the University of Ottawa in 2021. His research was on Coherent Nonlinear Raman microscopy and the applications of deep learning methods to extract quantitative information. During his studies, he was a visiting graduate research assistant at Molecular Photonics Group and Computational Laboratory for Energy and Nanoscience at the National Research Council of Canada. He is currently a research and development scientist at Few-Cycle Inc.





Agenda

1st Day

Date: Sep 14, 2022

17:00-19:00 (Montreal time)

Introduction to Machine Learning

linear regression

cost function

gradient descent optimization

generalization

overfitting/underfitting

feature engineering

a practical example of linear regression in Python

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2nd Day

Date: Sep 15, 2022

17:00-19:00 (Montreal time)

K-mean clustering algorithm

Principal Component Analysis

Python practice (implementation of k-mean clustering and PCA in Python)

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3rd Day

Date: Sep 16, 2022

17:00-19:00 (Montreal time)

Introduction to Neural Network

Convolutional Neural Network

Denoising Autoencoder

Using Autoencoder for classification

Application of Convolutional Autoencoder in Raman Microscopy