Practical Machine Learning and Rails Part1

Practical MachineLearning and Rails

Andrew Cantino VP Engineering, Mavenlink @tectonic Founder, Agile Productions @ryanstout

This talk will- introduce machine learning- make you ML-aware- have examples

This talk will not- give you a PhD- implement algorithms- cover collaborative filtering, optimization, clustering, advanced statistics, genetic algorithms, classical AI, NLP, ...

What is Machine Learning?Many different algorithmsthat predict datafrom other datausing applied statistics.

"Enhance and rotate 20 degrees"

What data? The web is data. User decisions APIs A/B Tests Databases Logs StreamsBrowser versions Reviews Clicktrails

Okay. We have data.What do we do with it?We classify it.

Classification

Classification OR

Classification :) OR :(

Classification• Documents o Sort email (Gmails importance filter) o Route questions to appropriate expert (Aardvark) o Categorize reviews (Amazon)• Users o Expertise; interests; pro vs free; likelihood of paying; expected future karma• Events o Abnormal vs. normal

Algorithms: Decision Tree Learning

Algorithms: Decision Tree Learning Features Email contains word "viagra" no yes Email contains Email contains word "Ruby" attachment? no yes no yes P(Spam)=10% P(Spam)=5% P(Spam)=70% P(Spam)=95% Labels

Algorithms: Support Vector Machines (SVMs) Graphics from Wikipedia

Algorithms: Naive Bayes• Break documents into words and treat each word as an independent feature• Surprisingly effective on simple text and document classification• Works well when you have lots of data Graphics from Wikipedia

Algorithms: Naive BayesYou received 100 emails, 70 of which were spam.Word Spam with this word Ham with this wordviagra 42 (60%) 1 (3.3%)ruby 7 (10%) 15 (50%)hello 35 (50%) 24 (80%)A new email contains hello and viagra. The probability that itis spam is:P(S|hello,viagra) = P(S) * P(hello,viagra|S) / P(hello,viagra) = 0.7 * (0.5 * 0.6) / (0.59 * 0.43) = 82% Graphics from Wikipedia

Algorithms: Neural Nets Hidden layerInput layer (features) Output layer (Classification) Graphics from Wikipedia

Curse of DimensionalityThe more features and labels that you have, the more data that you need. http://www.iro.umontreal.ca/~bengioy/yoshua_en/research_files/CurseDimensionality.jpg

Overfitting• With enough parameters, anything is possible.• We want our algorithms to generalize and infer, not memorize specific training examples.• Therefore, we test our algorithms on different data than we train them on.

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Practical Machine Learning and Rails Part1

by ryanstout on Apr 23, 2012

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