[talk] Machine Learning

[Sujit K M]

> One of the reasons machine learning is becoming popular is because big data
> is commodity now. Users can store vast amounts of data in "the cloud"
> BigQuery, Hadoop, Redshift, Oracle RAQ, etc. Ten years ago the dominant
> challenge was cheaply storing and querying data, but now that is becoming
> commodity. An enterprise can buy tableau and use Amazon Redshift, and have
> an analyst (or a non technical product manager) give them summary statistics
> untill all parties are blue in the face, 500 scheduled reports running a
> day.

Machine Learning might be that. But these don't solve an non
deterministic problem.

>
> You can not replace summary statistics with machine learning. A classic
> machine learning tool is linear regression which you can use to make
> predictions. You take a dataset and you train a model. That model can be
> used to make predictions.

Agree with this too. But you are scoping yourself primarily on present or past
you are not taking care of future. Which Machine Learning should be
used to solve.

>
> For example given: users with tens/hundreds/thousands of attributes (age,
> gender,...) and a bid request with (tens/hundreds/thousands) of attributes
> (time of day, url,...), what attributes can be used to predict the final bid
> price? Running one process (Linear Regression) that tells what attribute or
> combinations of attributes predicts the price, COULD BE easier/more simple
> then having humans attempt to figure it out by producing different sets of
> summary statistics and collectively deciding what to optimize on, and
> constantly re-evaluating the rules as the landscape changes.

This forms the basis of computing, We had a Calculator invented for
this purpose.

>
> Obviously there is hype cycle, not every problem needs machine learning to
> solve. But get readdyyy for a ::shocker:: not everything is solved by BSD
> port tree.

I would be only happy to see the day we can solve a problem say increase every
.001% of transactions.