Topic 11e
Artificial Neural Networks

Prior to Class

By the end of this topic students should be able to:

• Explain the concept of a perceptron.
• Given a simple perceptron model and set of inputs, identify the output of the perceptron.
• Given a simple perceptron model, explain the function of the perceptron (explain its outputs).
• Explain how multiple perceptrons are combined to form an artificial neural network (ANN).

• Readings
• Artificial Neural Networks - pp 593-598
• Videos
• Crash Course AI #2 - Supervised Learning with Neurons
• Dr. Schafer explains Neural Networks
• Dr. Schafer explains validation and bias in AI/Neural Networks
• Activities
  • Perceptron Worksheet
    • Answer Key

 

In class

Discussion

Let's walk through this idea for a bit.

Checking for Understanding

Answer the following questions from your textbook. The answers to all Q&E questions are in the back of your book in Appendix F.

• p 597, #1
• p 598, #2

Extra Help

• Crash Course AI #3 - Neural Networks and Deep Learning
  • We think it is helpful to watch the big picture portion through the 5:55 point.
  • Feel free to watch the remainder of the video if you are finding this interesting and helpful, but this is not required.
  Crash Course AI #4 - Training Neural Networks
  • This video explains how neural networks actually get trained. It's a bit mathy (more so than we feel you need for the learning outcomes in this course). But it does a great job of explaining this material at a level that even your high school students could understand.
• Crash Course AI #5 - You can train a Neural Network to read your handwriting
  • This video introduces a Colab Program that you can use to actually train a neural network. If you don't know anything about programming, this is way too much. But if you know a little bit you (or your students) might be able to do some pretty cool things with this.
• Crash Course AI #6 - Unsupervised Learning
  • In some ways this general topic could have happened as part of Topic 11c. But, this video frames it in the context of Neural Networks. So we put it here instead.
• If you feel like you would benefit from hearing/seeing some of this again, but in a different way, we highly suggest these videos:
  • Neural Network Examples (From the Crash Course in Statistics)
  • Neural Networks Explained (Goes in to the math/structure of the network in more detail)