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My name is Kyle Kastner. I am here to talk about the basics of machine learning.
What is machine learning?
I break it into two topics - automation and data analysis. The two approaches you see are people who want to automate - and people who want to inspect their processes and figure out what is making them tick and how to be more efficient and productive.
Applications
The applications for machine learning have exploded in the last 5 years. Hardware has caught up with ideas from the 80s and the future is very bright.
Speech processing

Image processing

Natural language processing

Advertising

Recommendations

Automation spectrum
-hand crafted rules
-Statistics

-machine learning proper

-deep learning

A test
Classifying a point
The manifold hypothesis

Classification
Drawing a boundary in some space.
Regression
You are trying to predict what something will do - not merely classifying into a group of labels
Learning functions
The core of machine learning is about learning functions.
Train - valid - test
On the left we have the red data - on the right blue data. You split a chunk of the red data and say it is the fake information I am going to test against. We really want our systems to work on new data.
What should I use?
Anaconda and Canopy. They have all the packages installed for you already.
Examples
Recommender systemsThe goal is - people rate some stuff and not other stuff. The goal is to automatically rate the stuff which has not been rated.
The example goes through a list of jokes.

Tip: Use pandas to read Excel files.

I implemented an algorithm called probabilistic matrix factorization.

Validation mean absolute error definition.

What the Recommender system reconstructs. The matrices tell us something - users are not completely unique. You can group them together.

Spam classification
We use tf-idf vectorizer and bernoulli naive bayes.
Digits
Shows the manifold structure we talked about.
Object detection
This uses deep neural networks.
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