Ml crash directory

Are you familiar with regression - https://m.youtube.com/watch?v=aq8VU5KLmkY?
One way to view Ml is regression on steroids...which mean a harder
optimization problem (one that does not have a close analytic solution
and/or is not convex) with many parameters.

Let's consider supervised learning first. You are given n labeled data points,

(
x1,y1),...,(xn,yn). Your objective is to find a function f(x)=y that
best predicts y on a new batch of x's. When y is continuous it is
called regression and when its discrete it is called classification.

There are two things to notice right away

1. To solve this an optimization problem is defined, e.g., a minimization of square error in our original regression problem

2.
Trying to explain the given data completely which is sometimes called
extrapolation is actually a pitfall, you may capture random trends and
your prediction power may be hindered. This is called overfitting

The
basic intuition underlying many approaches to the classification
problem is that had we known p(x, y) and given a new x we would have
calculated p(x, y) for each y and choose y with the greatest
probability. The difficulty is that it is not easy to estimate p(x,
y).

A simplifying independence assumption leads to the naive Bayes approach that is intuitively covered in the first part of Ariel
Kleiner's crash course on ML at http://ampcamp.berkeley.edu/wp-content/uploads/2012/06/ariel-kleiner-ampcamp-2012-machine-learning-part-1.pdf.

Yet
another approach is to define an optimization that attempts to maximize
performance on the training data while keeping f(x) simple. This is
done in a varsities of ways.

To deep dive on ML concepts see
reference three below. Iterate between reference three and simple ML
tutorial in python or R to master the subject.

References

1. Introduction to programmers on why ml is useful to master -

https://m.youtube.com/watch?v=0mK52UsOj-U

Ignores the challenges of applying it where it excels and dealing with drift.

2. Nice overview that start with classification https://m.youtube.com/watch?v=z-EtmaFJieY
only thing to be careful of is the claim that neural network are not
statistical models. Estimating a neural network performance should be
done using the same standard statistical tools, e.g., cross validation.

3. An intuitive deep dive on the concepts of machine learning is given by Haul Daume III at http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf

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