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CSE 163

ML and Images

��Hunter Schafer

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Questions During Class? Ask in Zoom chat!

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💬Before Class: Favorite board/card game?

🎵Music: Carly Rae Jepsen Dedicated Side B

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Machine Learning

Terms from machine learning

  • Features / labels
  • Learning algorithm
  • Model
  • Model class
  • Training set / test set
  • Parameters / Hyperparameters

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ML + Images

How do we do machine learning on images?

  • Simplest: Unroll the image into a vector
  • Complex: Use other tools to extract features from the images

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Raw Image

Unrolled Image

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ML + Images

Pros: Simple transformation (just a call to reshape!)

Cons: It loses the idea of “neighboring” pixels (up/down)

  • Most machine learning models don’t take position of the features into account
  • This is where more complex models like convolutional neural networks come in to encode that local information as features

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Despite these drawbacks, it can work in practice on some problems!

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Neural Network

Based on how our brains �work

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Example

What is the output for this neuron if the inputs are 0 for the first input and 1 for the second. The activation function is the step function (0 if negative, 1 otherwise). The bias should be subtracted from the weighted sum before applying the activation function.

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Unsupervised

Learning

So far, we have seen supervised machine learning, where we have to explicitly shown the algorithm the labels

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Unsupervised machine learning lets the algorithm try to learn trends on its own without providing explicit labels

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Examples

  • Clustering
  • Outlier detection

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Group Work:

Best Practices

When you first working with this group:

  • Turn on your mic / camera or introduce yourself in chat
    • We prefer mic/camera if available to encourage sense of human interaction :)
  • Share your name + where in the world you’re calling in from!
  • Elect one person to “drive” and share their screen for reference

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Tips:

  • Starts with making sure everyone agrees to work on the same problem
  • Make sure everyone gets a chance to contribute!
  • Ask if everyone agrees and periodically ask each other questions!
  • Call TAs over for help if you need any!

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