Machine Learning

Machine LearningLW/OB presentation

Machine learning ( ML ) is a field concerned withstudying and developing algorithms that perform better at a task as they gain experience ( but mostly I wanted to use this cool picture )

WARNING This presentation is seriously lacking slides, preparation and cool running examples.That being said. I know what I’m talking about ;)

What ML is reallyabout…

What ML is reallyabout…• ML is about data, andmodeling its distribution

What ML is reallyabout…• ML is about data, andmodeling its distribution• ML is about a tradeoffbetween model accuracyand predictive power

What ML is reallyabout…• ML is about data, andmodeling its distribution• ML is about a tradeoffbetween model accuracyand predictive power• ML is about findingsimple yet expressiveclasses of distributions

What ML is reallyabout…• ML is about data, andmodeling its distribution• ML is about a tradeoffbetween model accuracyand predictive power• ML is about findingsimple yet expressiveclasses of distributions• ML is about usingapproximate numericalmethods to performBayesian update on thetraining data

ML = intersection of

Data sizes vary…

Data sizes vary…From a couple kilobytes

Data sizes varyFrom a couple kilobytes To petabytes

Type of problems solved• Supervised• Unsupervised• Reinforcement learning• ( transduction )

Type of problems solved• Supervised – Classification – Regression• Unsupervised• Reinforcement learning• ( transduction )

Type of problems solved• Supervised• Unsupervised - Clustering - Discovering causal links• Reinforcement learning• ( transduction )

Type of problems solved• Supervised• Unsupervised• Reinforcement learning – Learn to perform a task, only from final result• ( transduction ) – Not discussed, improve supervised learning with unsupervised samples

Typical applications• Image, speech, pattern recognition• Collaborative filtering• Time series forecasting• Game playing• Denoising• Any task where experience is valuable

Common ML techniques

Common ML techniques• Linearregression

Common ML techniques• Linearregression• Factor models

Common ML techniques• Linearregression• Factor models• Decision trees

Common ML techniques• Linearregression• Factor models• Decision trees• Neural networks

Common ML techniques• Linearregression• Factor models• Decision trees• Neural networksperceptron, multilayer perceptron with backpropagation,hebbian autoassociative memory, Boltzmann machine,spiking neurons…

Common ML techniques• Linearregression• Factor models• Decision trees• Neural networks• SVM’s

Common ML techniques• Linearregression• Factor models• Decision trees• Neural networks• SVM’s• Bayesiannetworks, whitebox models…

Meta-Methods

Meta-Methods– Ensemble forecasting

Meta-Methods– Ensemble forecasting– Bootstrapping, Bagging, model averaging

Meta-Methods– Ensemble forecasting– Bootstrapping, Bagging, model averaging– Boosting

Meta-Methods– Ensemble forecasting– Bootstrapping, Bagging, model averaging– Boosting– Inductive bias through

Meta-Methods– Ensemble forecasting– Bootstrapping, Bagging, model averaging– Boosting– Inductive bias through • Out of sample testing

Meta-Methods– Ensemble forecasting– Bootstrapping, Bagging, model averaging– Boosting– Inductive bias through • Out of sample testing • Minimum description length

Neural networks demystified

Neural networksdemystified• Perceptron ( 1957 )

Neural networksdemystified• Perceptron ( 1957 ) THIS IS…

Neural networksdemystified• Perceptron ( 1957 ) THIS IS… LINEAR ALGEBRA!

Neural networksdemystified• Perceptron• Linear separability

Neural networksdemystified• Perceptron• Linear separability8 binary inputs =>1/2212classifications linearlyseparable

Neural networksdemystified• Perceptron ( 1957 )• Linear separability• Multilayeredperceptron +backpropagation

Neural networksdemystified• Perceptron ( 1957 )• Linear separability• Multilayeredperceptron +backpropagation( 1969 ~ 1986 )

Neural networksdemystified• Perceptron ( 1957 )• Linear separability• Multilayeredperceptron +backpropagation• Smooth Interpolation

Many more types…

SVM in a nutshell

SVM in a nutshell• Maximize margin

SVM in a nutshell• Maximize margin• Embed in a high dimensional space

Ensemble learning• Combine predictions through voting ( with classifiers ) or regression to improve prediction

Ensemble learning• Combine predictions through voting ( with classifiers ) or regression to improve prediction• Train on random ( with replacement ) subsets of the data ( bootstrapping )

Ensemble learning• Combine predictions through voting ( with classifiers ) or regression to improve prediction• Train on random ( with replacement ) subsets of the data ( bootstrapping )• Or weight the data according to the quality of prediction, and train new weak classifiers accordingly ( boosting )

Numerical tricks

Numerical tricks• Optimization of fit with standard operational search techniques

Numerical tricks• Optimization of fit with standard operational search techniques• EM algorithm

Numerical tricks• Optimization of fit with standard operational search techniques• EM algorithm• MCMC methods ( Gibbs sampling, metropolis algorithm… )

A fundamental Bayesian model, the Hidden Markov Model

A fundamental Bayesian model, the Hidden Markov Model• Hidden states produce observed states

A fundamental Bayesian model, the Hidden Markov Model• Hidden states produce observed states• Billions of application – Finance – Speech recognition – Swype – Kinect – Open heart surgery – Airplane navigation

Questions I was asked• How does Boosting work ?• What is the No Free Lunch Theorem ?• Writing style recognition• Signature recognition• Rule extraction• Moving odds in response to informed gamblers• BellKor-Pragmatic Chaos and the Netflix prize

Writing style recognition

Writing style recognition• Naïve Bayes ( similar to spam filtering, bag of words approach )

Writing style recognition• Naïve Bayes ( similar to spam filtering, bag of words approach )• Clustering of HMM model parameters

Writing style recognition• Naïve Bayes ( similar to spam filtering, bag of words approach )• Clustering of HMM model parameters• Simple statistics on text corpus ( sentence length distribution, word length distribution, density of punctuation )

Writing style recognition• Naïve Bayes ( similar to spam filtering, bag of words approach )• Clustering of HMM model parameters• Simple statistics on text corpus ( sentence length distribution, word length distribution, density of punctuation )• Combine with a logistic regression

Signature recognition

Signature recognition• Depends if raster or vector

Signature recognition• Depends if raster or vector• Post office uses neural networks, but corpus is gigantic

Signature recognition• Depends if raster or vector• Post office uses neural networks, but corpus is gigantic• Dimensionality reduction is key

Signature recognition• Wavelet on raster image for feature extraction

Signature recognition• Depends if raster or vector• Post office uses neural networks, but corpus is gigantic• Dimensionality reduction is key• Wavelet on raster image for feature extraction• Path following then learning on path features ( total variation, average curvature etc )

Rules extraction• Hard, hypothesis space not smooth• Decision tree regression• Genetic Programming ( Koza )

Netflix prize

Netflix prize• The base (cinematch) = latent semantic model• The defining characteristic of winners, ensemble prediction with neural networks to combine predictors• Best team were mergers of good teams

Latent semantic model• There is a set of K “features”. Each movie has a score in each feature, each user has a weight for each feature.• Features are latent, we only assume the value of K.• Equivalent to representing the rating matrix as a product of a score and preference matrix. SVD minimizes RMSE

Poker is hard…

Poker is hard…• Gigantic, yet not continuous state space• Dimensionality reduction isn’t easy• High variance• Possible to make parametric strategies and optimize with ML• Inputs such as pot odds trivial to compute

Uhuh, slides end here

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Machine Learning

by Arthur Breitman

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