AI News, Machine Learning is Fun!

Machine Learning is Fun!

Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem.

The same classification algorithm used to recognize handwritten numbers could also be used to classify emails into spam and not-spam without changing a line of code.

But there’s a problem — you can glance at a house and have a pretty good idea of what a house is worth, but your trainees don’t have your experience so they don’t know how to price their houses.

To help your trainees (and maybe free yourself up for a vacation), you decide to write a little app that can estimate the value of a house in your area based on it’s size, neighborhood, etc, and what similar houses have sold for.

But most importantly, you write down the final sale price: Using that training data, we want to create a program that can estimate how much any other house in your area is worth: This is called supervised learning.

This kind of like having the answer key to a math test with all the arithmetic symbols erased: From this, can you figure out what kind of math problems were on the test?

This is kind of like someone giving you a list of numbers on a sheet of paper and saying “I don’t really know what these numbers mean but maybe you can figure out if there is a pattern or grouping or something — good luck!” So what could do with this data?

Maybe you’d find out that home buyers in the neighborhood near the local college really like small houses with lots of bedrooms, but home buyers in the suburbs prefer 3-bedroom houses with lots of square footage.

In fact, unsupervised learning is becoming increasingly important as the algorithms get better because it can be used without having to label the data with the correct answer.

If you sell houses for a long time, you will instinctively have a “feel” for the right price for a house, the best way to market that house, the kind of client who would be interested, etc.

Of course if you are reading this 50 years in the future and we’ve figured out the algorithm for Strong AI, then this whole post will all seem a little quaint.

If you didn’t know anything about machine learning, you’d probably try to write out some basic rules for estimating the price of a house like this: If you fiddle with this for hours and hours, you might end up with something that sort of works.

Who cares what exactly the function does as long is it returns the correct number: One way to think about this problem is that the price is a delicious stew and the ingredients are the number of bedrooms, the square footage and the neighborhood.

That would reduce your original function (with all those crazy if’s and else’s) down to something really simple like this: Notice the magic numbers in bold — .841231951398213, 1231.1231231, 2.3242341421, and 201.23432095.

dumb way to figure out the best weights would be something like this: Start with each weight set to 1.0: Run every house you know about through your function and see how far off the function is at guessing the correct price for each house: For example, if the first house really sold for $250,000, but your function guessed it sold for $178,000, you are off by $72,000 for that single house.

Here’s one way: First, write a simple equation that represents Step #2 above: Now let’s re-write exactly the same equation, but using a bunch of machine learning math jargon (that you can ignore for now): This equation represents how wrong our price estimating function is for the weights we currently have set.

If we graph this cost equation for all possible values of our weights for number_of_bedrooms and sqft, we’d get a graph that might look something like this: In this graph, the lowest point in blue is where our cost is the lowest — thus our function is the least wrong.

It’s easy to come up with a set of weights that always works perfectly for predicting the prices of the houses in your original data set but never actually works for any new houses that weren’t in your original data set.

Once you start seeing how easily machine learning techniques can be applied to problems that seem really hard (like handwriting recognition), you start to get the feeling that you could use machine learning to solve any problem and get an answer as long as you have enough data.

For example, if you build a model that predicts home prices based on the type of potted plants in each house, it’s never going to work.

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