https://lh7-us.googleusercontent.com/-l1FEXmi77NeDcZP0VGZ_8eChTrNvcaOgGHtoGimGRpr7gQoJ8NOrzbaxs0Z-UQoqWdMCmF7LjuVkknMXfWqZjiRAT_p7dcLTpFCfBysx7dRubzkyX0riN7eD5EC_ozKWRDl_QlQT_cdC_JaSk6Rh1M

Objectives

If all goes well you’ll come out of this with:

• High level understanding of ML models; what type of problems they’re suited well for solving
• Their strengths, weaknesses and limitations
• The tools and confidence to explore ML on your own should you choose to

You won’t be able to:

• Literally be able to ship an ML model from scratch in 30 minutes
• Use words like heteroscedasticity

Notes

We’re gonna start with classical (“non-deep”), supervised machine learning for the sake of this demo (partly because it’s easier to start there and build on top, partly because I don’t have as many coherent jokes and metaphors for deep learning). If none of those words make sense yet that’s okay.

Wtf is ML

A wonderful cliche is starting monologues with a definition. Let’s run it back.

“Machine learning is a subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed. “

A history of rapid scale decision-making

Let’s say I need to decide how much a house is worth. You’d go, gather a bunch of data. How much square footage the house has, how much homes nearby are worth. Maybe check whether the drive-way sits at the crossroads of a railroad and an interstate.

At 1 house a day you do the math yourself. Maybe start with a spreadsheet, model it out, and apply some judgment on top.

At 10 homes a day maybe you hire a person.

At 1000/day you have a team, along with a bunch of systems (software, managers, etc) in to ensure accuracy and consistency between the folks you hire.

At 1000/minute you probably want a computer.