Practical Machine Learning and Rails Part1

Apr 23, 2012Download as pptx, pdf7 likes4,014 views

This talk will provide a high-level introduction to machine learning concepts and examples without getting into advanced theory or implementation details. It will explain that machine learning uses applied statistics to predict data from other data using various algorithms like decision trees, support vector machines, and naive Bayes. It will give examples of how these algorithms can be used to classify documents, users, and events but will not provide in-depth training or cover many specific machine learning domains and techniques. The talk aims to make audiences aware of machine learning concepts rather than providing expert-level knowledge.

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 algorithms

that predict data

from other data

using applied statistics.

What data?
The web is data.

User decisions
APIs A/B Tests
Databases
Logs Streams

Browser versions
Reviews
Clicktrails

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

We classify it.

Classification
• Documents
o Sort email (Gmail's 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

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 Bayes

You received 100 emails, 70 of which were spam.
Word Spam with this word Ham with this word

viagra 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 it
is 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 layer

Input layer (features)

Output layer (Classification)

Graphics from Wikipedia

Curse of Dimensionality

The 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

1. Practical Machine Learning and Rails

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

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

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

5. What is Machine Learning? Many different algorithms that predict data from other data using applied statistics.

6. "Enhance and rotate 20 degrees"

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

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

9. Classification

10. Classification OR

11. Classification :) OR :(

12. Classification • Documents o Sort email (Gmail's 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

13. Algorithms: Decision Tree Learning

14. 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

15. Algorithms: Support Vector Machines (SVMs) Graphics from Wikipedia

16. Algorithms: Support Vector Machines (SVMs) Graphics from Wikipedia

17. 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

18. Algorithms: Naive Bayes You received 100 emails, 70 of which were spam. Word Spam with this word Ham with this word viagra 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 it is 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

19. Algorithms: Neural Nets Hidden layer Input layer (features) Output layer (Classification) Graphics from Wikipedia

20. Curse of Dimensionality The 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

21. 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.

Practical Machine Learning and Rails Part1

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