08_learning-intro.pdf - What is Learning Machine Learning Introduction and Unsupervised Learning • “Learning is making useful changes in our

08_learning-intro.pdf - What is Learning Machine Learning...

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1 Machine Learning: Introduction and Unsupervised Learning Chapter 18.1 , 18.2, 18.8.1 and “ Introduction to Statistical Machine Learning 1 What is Learning? “Learning is making useful changes in our minds” Marvin Minsky “Learning is constructing or modifying representations of what is being experienced“ – Ryszard Michalski “Learning denotes changes in a system that ... enable a system to do the same task more efficiently the next time” Herbert Simon 3 Why do Machine Learning? Solve classification problems Learn models of data (“data fitting”) Understand and improve efficiency of human learning (e.g., Computer-Aided Instruction (CAI)) Discover new things or structures that are unknown to humans (“data mining”) Fill in skeletal or incomplete specifications about a domain 4 Major Paradigms of Machine Learning Rote Learning Induction Clustering Discovery Genetic Algorithms Reinforcement Learning Transfer Learning Learning by Analogy Multi-task Learning 5
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2 Inductive Learning Generalize from a given set of ( training ) examples so that accurate predictions can be made about future examples Learn unknown function: f ( x ) = y x : an input example (aka instance ) y : the desired output Discrete or continuous scalar value h (hypothesis) function is learned that approximates f 6 Representing “Things” in Machine Learning An example or instance , x , represents a specific object ( thing ) x often represented by a D- dimensional feature vector x = ( x 1 , . . . , x D ) Each dimension is called a feature or attribute Continuous or discrete valued x is a point in the D -dimensional feature space Abstraction of object. Ignores all other aspects (e.g., two people having the same weight and height may be considered identical) 7 Feature Vector Representation Preprocess raw data extract a feature (attribute) vector, x , that describes all attributes relevant for an object Each x is a list of ( attribute , value ) pairs x = [( Rank , queen ), ( Suit , hearts ), ( Size , big )] number of attributes is fixed: Rank, Suit, Size number of possible values for each attribute is fixed (if discrete) Rank : 2, …, 10, jack, queen, king, ace Suit : diamonds, hearts, clubs, spades Size : big, small 8 Types of Features Numerical feature has discrete or continuous values that are measurements, e.g., a person’s weight Categorical feature is one that has two or more values (categories), but there is no intrinsic ordering of the values, e.g., a person’s religion (aka Nominal feature) Ordinal feature is similar to a categorical feature but there is a clear ordering of the values, e.g., economic status, with three values: low, medium and high 9
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3 Feature Vector Representation Each example can be interpreted as a point in a D -dimensional feature space, where D is the number of features/attributes Suit Rank spades clubs hearts diamonds 2 4 6 8 10 J Q K 10 Feature Vector Representation Example Text document
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