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.
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:
- A. D. Babu, “Artificial Intelligence vs Machine Learning vs Deep Learning (AI vs ML vs DL),” Medium, Nov. 06, 2019. https://medium.com/@alanb_73111/artificial-intelligence-vs-machine-learning-vs-deep-learning-ai-vs-ml-vs-dl-e6afb7177436
- “ASmarine,” www.facebook.com. https://www.facebook.com/ASMARINE20/photos (accessed Oct. 22, 2022).
- Wikipedia Contributors, “Self-driving car,” Wikipedia, Mar. 25, 2019. https://en.wikipedia.org/wiki/Self-driving_car
- K. Shamaei, G. S. Sawicki, and A. M. Dollar, “Estimation of Quasi-Stiffness of the Human Hip in the Stance Phase of Walking,” PLoS ONE, vol. 8, no. 12, p. e81841, Dec. 2013, doi: 10.1371/journal.pone.0081841.
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