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## Machine Learning Use Cases

Keith VanderLinden<br>
Calvin University

## Machine Learning

Machine learning is an approach to *learn complex patterns* from *existing data* and use these patterns to make *predictions on unseen data*.

::: {.notes}
The next slides talk through this definition with respect to the example.

References:
- DMLS Chapter 1
:::

## Learning Complex Patterns

The patterns should be sufficiently complicated and changeable that they can&rsquo;t be pre-specified. Distinguish:

- Traditional Programming<br>
 `inputs + algorithms = outputs`

- Machine learning<br>
`inputs + outputs = patterns`

The former paradigm works better for computable/consistent phenomena; the latter for hard-to-compute/changeable phenomena.

::: {.notes}
This has been a real paradigm shift. We spent a half-century trying to program ChatGPT; we trained an ML system to learn it around a week's computation.

References:
- The Peter Norvig video on this issue.
- DMLS Figure 1-2.
:::

## Finding Existing Data

There must be data for learning that&rsquo;s:

- Appropriate - Supervised leaning requires input/output pairs.
- Voluminous - ML systems need lots of learning examples.
- Balanced - Skewed data produces skewed results.
- Available - The data must be (ethically) available.

::: {.notes}

:::

## Predicting Unseen Data

Predictions must be:

- Possible - The past and future must have similar distributions.
- Valuable - We actually need to care about what&rsquo;s being predicted.
- Non-Mission-Critical - We need to be able to live with incorrect predictions.

::: {.notes}

:::
