learning from observations inductive learning - learning from examples machine learning
TRANSCRIPT
Learning from observations
Inductive Learning - learning from examples
Machine Learning
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What Is Machine Learning?“Logic is not the end of wisdom, it is just the beginning” --- Spock
System
Knowledge
Environment
Action1
time
Knowledge
Environment
System
changed
same
Action2
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Learning & Adaptation
• ”Modification of a behavioral tendency by expertise.” (Webster 1984)
• ”A learning machine, broadly defined is any device whose actions are influenced by past experiences.” (Nilsson 1965)
• ”Any change in a system that allows it to perform better the second time on repetition of the same task or on another task drawn from the same population.” (Simon 1983)
• ”An improvement in information processing ability that results from information processing activity.” (Tanimoto 1990)
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Ways humans learn things …talking, walking, running… Learning by mimicking, reading or being told facts Tutoring Being informed when one is correct Experience Feedback from the environment Analogy Comparing certain features of existing knowledge
to new problems Self-reflection Thinking things in ones own mind, deduction,
discovery
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A few achievements
Programs that can: Recognize spoken words Predict recovery rates of pneumonia
patients Detect fraudulent use of credit cards Drive autonomous vehicles Play games like backgammon –
approaching the human champion!
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Machine Learning
Machine learning involves automatic procedures that learn a task from a series of examples
Most convenient source of examples is data
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Learning
Definition: A computer program is said to learn
from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience.
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Machine Learning Models Classification Clustering Regression Time series analysis Association Analysis Sequence Discovery ….
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ClassificationAssign items to one of a set of predefined classes of objects based on a set of observed features
Text
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ClassificationAssign items to one of a set of predefined classes of objects based on a set of observed features
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ClusteringSeeks to place objects into meaningful groups automatically, based on their similarity. Does not require the groups to be predefined. The hope in applying clustering algorithms is that they will discover useful but unknown classes of items.
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Classification example
Model
Test setTrain set
Learning system
New data
LoanYes/No
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Inductive learning Simplest form: learn a function from examples
f is the target function
An example is a pair (x, f(x))
Problem: find a hypothesis hsuch that h ≈ fgiven a training set of examples
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Inductive learning method Construct/adjust h to agree with f on training set (h is consistent if it agrees with f on all examples) E.g., curve fitting:
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Inductive learning method Construct/adjust h to agree with f on training set (h is consistent if it agrees with f on all examples) E.g., curve fitting:
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Inductive learning method Construct/adjust h to agree with f on training set (h is consistent if it agrees with f on all examples) E.g., curve fitting:
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Inductive learning method Construct/adjust h to agree with f on training set (h is consistent if it agrees with f on all examples) E.g., curve fitting:
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Inductive learning method Construct/adjust h to agree with f on training set (h is consistent if it agrees with f on all examples) E.g., curve fitting:
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Inductive learning method Construct/adjust h to agree with f on training set (h is consistent if it agrees with f on all examples) E.g., curve fitting:
Ockham’s razor: prefer the simplest hypothesis consistent with data
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Machine Learning Methods Instance Based Methods (CBR, k-NN) Decision Trees Artificial Neural Networks Bayesian Networks Naïve Base Evolutionary Strategies Support Vector Machines ..
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Classification example
Weight
Heighto
xx
xx
xx
xx x
x
xo
ooo
o oo
oo
x
oox
x
x - weight-lifters
o - ballet dancers
Features: height, weight
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Classification example - Simple Model
Weight
Heighto
xx
xx
xx
xx x
x
xo
ooo
o oo
oo
x
oox
x
x - weight-lifters
o - ballet dancers
Decision boundaryDecision boundary
Features: height, weight
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Classification example - Complex model
Weight
Heighto
xx
xx
xx
xx x
x
xo
ooo
o oo
oo
x
oox
x
x - weight-lifters
o - ballet dancers
Complex Decision boundaryComplex Decision boundary
Features: height, weight
Note: A simple decision boundary is better than a Note: A simple decision boundary is better than a complex one - It GENERALIZES better.complex one - It GENERALIZES better.
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Learning Paradigms
Supervised learning - with teacher inputs and correct outputs are provided by the teacher
Reinforced learning - with reward or punishment an action is evaluated
Unsupervised learning - with no teacher no hint about correct output is given
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Nearest Neighbor Simple effective approach for supervised
learning problems
Envision each example as a point in n-dimensional space- Picture with 2 of them
Classify test point same as nearest training point (Euclidean distance)
Patient ID # of Tumors Avg Area Avg Density Diagnosis1 5 20 118 Malignant2 3 15 130 Benign3 7 10 52 Benign4 2 30 100 Malignant
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k-Nearest Neighbor Nearest Neighbor can be subject to
noise Incorrectly classified training points Training anomalies
k-Nearest Neighbor Find k nearest training points (k odd)
and vote on which classification
Works on numerical data