This document provides an overview of machine learning. It discusses that machine learning is concerned with developing algorithms that perform better at tasks with experience. It then covers what machine learning is really about, including modeling data distributions, balancing accuracy and predictive power, and using approximate numerical methods to perform Bayesian updates. Common machine learning techniques like linear regression, decision trees, neural networks, and SVMs are also summarized.

1. Machine Learning
LW/OB presentation

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

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

4. What ML is really
about…

5. What ML is really
about…
• ML is about data, and
modeling its distribution

6. What ML is really
about…
• ML is about data, and
modeling its distribution
• ML is about a tradeoff
between model accuracy
and predictive power

7. What ML is really
about…
• ML is about data, and
modeling its distribution
• ML is about a tradeoff
between model accuracy
and predictive power
• ML is about finding
simple yet expressive
classes of distributions

8. What ML is really
about…
• ML is about data, and
modeling its distribution
• ML is about a tradeoff
between model accuracy
and predictive power
• ML is about finding
simple yet expressive
classes of distributions
• ML is about using
approximate numerical
methods to perform
Bayesian update on the
training data

9. ML = intersection of

10. Data sizes vary…

11. Data sizes vary…
From a couple kilobytes

12. Data sizes vary
From a couple kilobytes To petabytes

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

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

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

16. 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

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

18. Common ML techniques

19. Common ML
techniques
• Linear
regression

20. Common ML
techniques
• Linear
regression
• Factor models

21. Common ML
techniques
• Linear
regression
• Factor models
• Decision trees

22. Common ML
techniques
• Linear
regression
• Factor models
• Decision trees
• Neural networks

23. Common ML
techniques
• Linear
regression
• Factor models
• Decision trees
• Neural networks
perceptron, multilayer perceptron with backpropagation,
hebbian autoassociative memory, Boltzmann machine,
spiking neurons…

24. Common ML
techniques
• Linear
regression
• Factor models
• Decision trees
• Neural networks
• SVM’s

25. Common ML
techniques
• Linear
regression
• Factor models
• Decision trees
• Neural networks
• SVM’s
• Bayesian
networks, white
box models…

26. Meta-Methods

27. Meta-Methods
– Ensemble forecasting

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

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

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

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

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

33. Neural networks demystified

34. Neural networks
demystified
• Perceptron ( 1957 )

35. Neural networks
demystified
• Perceptron ( 1957 )
THIS IS…

36. Neural networks
demystified
• Perceptron ( 1957 )
THIS IS…
LINEAR
ALGEBRA!

37. Neural networks
demystified
• Perceptron
• Linear separability

38. Neural networks
demystified
• Perceptron
• Linear separability
8 binary inputs =>
1/2212classifications linearly
separable

39. Neural networks
demystified
• Perceptron ( 1957 )
• Linear separability
• Multilayered
perceptron +
backpropagation

40. Neural networks
demystified
• Perceptron ( 1957 )
• Linear separability
• Multilayered
perceptron +
backpropagation
( 1969 ~ 1986 )

41. Neural networks
demystified
• Perceptron ( 1957 )
• Linear separability
• Multilayered
perceptron +
backpropagation
• Smooth Interpolation

42. Many more types…

43. SVM in a nutshell

44. SVM in a nutshell
• Maximize margin

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

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

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

48. 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 )

49. Numerical tricks

50. Numerical tricks
• Optimization of fit with standard
operational search techniques

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

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

53. A fundamental Bayesian model,
the Hidden Markov Model

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

55. 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

56. 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

57. Writing style recognition

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

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

60. 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 )

61. 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

62. Signature recognition

63. Signature recognition
• Depends if raster or vector

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

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

66. Signature recognition
• Wavelet on raster image for feature
extraction

67. 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 )

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

69. Netflix prize

70. 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

71. 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

72. Poker is hard…

73. 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

74. Uhuh, slides end here

75. Sort of… Questions ?