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 base approach that is intuitively covered in the first part of Ariel Kleiner's crash course on ML at http://ampcamp.berkeley.edu/wpcontent/uploads/2012/06/arielkleinerampcamp2012machinelearningpart1.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=0mK52UsOjU
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=zEtmaFJieY 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/cimlv0_8all.pdf
Monday, June 25, 2018
Sunday, June 10, 2018
Will discuss the kernel trick and SVM. We have recorded some of it here Ihttps://www.youtube.com/playlist?list=PLRPue8gCw668mizHl7s0ZQzATzdL8FZJ
Justification for the basic hard SVM optimization problem can be found here
https://www.youtube.com/playlist?list=PLRPue8gCw668mizHl7s0ZQzATzdL8FZJ
Justification for the basic hard SVM optimization problem can be found here
https://www.youtube.com/playlist?list=PLRPue8gCw668mizHl7s0ZQzATzdL8FZJ
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Under the Bayesian setting, probabilities represent our brief on the state of the world which we can update incrementally after each experim...

We'll continue with convex optimization  https://drive.google.com/drive/folders/0BzUXUMab8u_ZU0h3ZEc5Z2VrMm8

Bayesian inference recording . For more details see chapter 24 in the understanding book.

Back to Bayesian inference  https://drive.google.com/file/d/1NUioDotuKeA8kKg341qRjyUESnUjxkos/view?usp=sharing