Post on 16-Nov-2014
description
Machine Learning Overview
Ricardo Wendell
June 2013
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Artificial Intelligence & Machine Learning Artificial Neural Networks Clustering Genetic Algorithms Reinforcement Learning Q&A
Agenda
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Artificial Intelligence & Machine Learning
What is Intelligence?
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What’s involved in intelligence?
ü Ability to interact with the real world
ü Reasoning and planning
ü Learning and adaptation
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What’s involved in intelligence?
ü Ability to interact with the real world
ü Reasoning and planning
ü Learning and adaptation
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“To learn is to gain knowledge, or
understanding of, or skill in, by study, instruction, or experience”
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Machine Learning
Field of study that gives computers the ability to learn without being explicitly programmed
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Why?
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Why?
Why should machines have to learn?
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Why?
Why should machines have to learn? Why not design machines to perform as desired in the first place?
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Some reasons…
There are tasks that cannot be defined well except by example
It is possible that hidden among large piles of data are important relationships and correlations
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An example
13 http://googleresearch.blogspot.com.br/2012/08/speech-recognition-and-deep-learning.html
Google’s Deep Learning
How can machines learn?
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Types of learning
Supervised
Types of learning
Unsupervised
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Supervised
Ways of address these problems…
ü Statistics
ü Brain Models
ü Adaptive Control
ü Evolutionary Models
ü Psychological Models
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Ways of address these problems…
ü Statistics
ü Brain Models
ü Adaptive Control
ü Evolutionary Models
ü Psychological Models
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Artificial Neural Networks
Biological Inspiration
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McCulloch-Pitts Neuron (1943)
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Multilayer Perceptron
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How do we train it?
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Backpropagation Algorithm initialize the weights in the network
do
for each example e in the training set
O = neural-‐net-‐output(network, i)
T = teacher output for e
compute error (T -‐ O) at the output units
compute delta_wh for all weights from hidden layer to output layer
compute delta_wi for all weights from input layer to hidden layer
update the weights in the network
until all examples classified correctly or stopping criterion satisfied
return the network
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Recurrent ANN
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Drawbacks
ANNs with many hidden layers can solve difficult problems… but are very hard to train!
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Deep Learning http://www.kurzweilai.net/how-bio-inspired-deep-learning-keeps-winning-competitions 27
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Clustering
Clustering algorithms
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Clustering problems
http://jsfiddle.net/8NpNp/27/ 30
Many applications
Medical imaging
Market research
Climatology
Social networks analysis
Recommender systems
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Genetic Algorithms
Given a population, how can we find the best
group of individuals that satisfy some criteria?
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Given a population, how can we find the best
group of individuals that satisfy some criteria?
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Typical requirements
a genetic representation of the solution domain
a fitness function to evaluate the solution domain
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Genetic representation
Each solution is represented as a “chromosome”
Each solution has a fitness value
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Generic Genetic Algorithm
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Example: Mona Lisa from 1500 characters
38 http://www.youtube.com/watch?v=TManzvC9pi8&NR
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Reinforcement Learning
Main concepts
Inspired by behaviorist psychology
Learning by interacting with the environment
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Suited for problems which include a long-term versus short-term reward trade-off
How to teach a computer to play games?
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Markov Decision Processes
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How to flip pancakes?
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http://www.youtube.com/watch?v=W_gxLKSsSIE
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Conclusion
Conclusion There are many techniques…
Results are heavily influenced by input representation
It’s a math-heavy field!
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Some libraries
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http://www.youtube.com/watch?v=WB9zr0IZCPQ
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Q&A