This site contains learning materials for two AI Workshops run by New Mexico State University Klipsch School of Electrical and Computer Engineering and the USDA Agricultural Research Service:

  • AI Workshop 1: Intro to Image Processing, Classical Machine Learning, and Deep Learning
  • AI Workshop 2: Advanced Topics in Deep Learning for Image Proccessing

The workshops cover the basics of image processing using classical machine learning techniques as well as provide more in-depth exploration of some common deep learning architectures used in image processing. Participants are introduced to the basic concepts via powerpoint lecture and guided through hands-on programming in an interactive jupyter notebook framework.

Before you come

Before you come

Get your computer set up before class begins

Set me up!

Learn

Learn

Workshop lessons and exercises

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Archive

Archive

Older materials from previously taught sessions

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Learning Goals

By the end of Workshop 1 participants should be able to:

1) display and interpret grayscale and color images,
2) apply common image transforms and filters to images,
3) extract hand-designed features from an image dataset and format those features for use in machine learning,
4) apply common machine learning classifiers to an image dataset and assess performance,
5) define a convolutional neural network (CNN) for classification of images, including pre-processing of the input data, and
6) train and test a CNN for classification of images, including implementation of a simple transfer learning.

By the end of Workshop 2 participants should be able to:

1) visualize characteristics of a CNN to help interpret performance,
2) modify a CNN architecture for application to new data (application of more complex transfer learning), and
3) apply methods learned for classification CNNs to other forms of CNNs, e.g., image segmentation, object detection.