Blog Post

Introduction to Machine Learning (ML)

Welcome to the desert of the real. We are excited to announce that following the requests from our Mecharithm family to design a Machine Learning (ML) course, especially for roboticists, we are now able to provide you with a machine learning course specifically designed for robotics applications. 

Introduction Machine Learning-pic-1

Here are the contents of the whole course on machine learning for robotics:

  • Quick crash course on Python
  • The linear regression problem + small application
  • The logistic regression problem + application on image classification
  • Introduction to supervised learning: Neural networks 1
  • Introduction to supervised learning: Neural networks 2
  • Introduction to supervised learning: Neural networks 3
  • Introduction to supervised learning: Support vector machines
  • A case study on classification using supervised learning methods
  • Unsupervised learning: K-nearest neighbor and clustering applications
  • Deep neural networks and new trends in machine learning
  • Introduction to Tensorflow
  • A quick guide on hyper-parameter tuning and performance optimization
  • A case study on deep neural networks (DNN)
  • Convolutional Neural Networks (CNN) and image processing
  • A case study on CNNs

Course prerequisites:

Basic knowledge of high school calculus and algebra

Course software tools/utilities (all to be taught during the course, no need to prepare):

  • Ubuntu 20.04
  • Anaconda
  • Spyder
  • Python3
  • Tensorflow
  • Additional libraries (numpy, scipy, etc.)

Course expectations:

By the end of this course, you will be able to:

  • Apply basic machine learning algorithms using Python
  • Understand how virtual environments work
  • Understand supervised and unsupervised learning methods
  • Work on famous deep learning applications in robotics
  • Use TensorFlow as a machine learning platform
  • Understand the behavior of a neural network and apply critical thinking to improve performance
  • Use CNNs for computer vision applications
  • Read scientific papers on deep learning and implement their proposed approaches

The first lesson is a short introduction to Machine Learning (ML), what it is, why robotics engineers should study it, and a short introduction to the deep learning era. Here is the first video, which is an introduction to Machine Learning (ML):

References for the first lesson:

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