machine learning queens college lecture 1: introduction
TRANSCRIPT
Machine Learning
Queens College
Lecture 1: Introduction
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Today
• Welcome• Overview of Machine Learning• Class Mechanics• Syllabus Review
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My research and background
• Speech– Analysis of Intonation– Segmentation
• Natural Language Processing– Computational Linguistics
• Evaluation Measures• All of this research relies heavily on
Machine Learning
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You
• Why are you taking this class?• What is your background and comfort with
– Calculus– Linear Algebra– Probability and Statistics
• What is your programming language of preference?– C++, java, or python are preferred
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Machine Learning
• Automatically identifying patterns in data• Automatically making decisions based on
data• Hypothesis:
Data Learning Algorithm Behavior
Data Programmer or Expert Behavior
≥
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Machine Learning in Computer Science
Machine LearningBiomedical/
ChemedicalInformatics
Financial Modeling
Natural Language Processing
Speech/Audio
Processing Planning
Locomotion
Vision/Image
Processing
Robotics
Human Computer Interaction
Analytics
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Major Tasks
• Regression– Predict a numerical value from “other
information”
• Classification– Predict a categorical value
• Clustering– Identify groups of similar entities
• Evaluation
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Feature Representations
• How do we view data?
Entity in the World
Web PageUser BehaviorSpeech or Audio DataVisionWinePeopleEtc.
Feature Representation
Machine Learning Algorithm
Feature Extraction
Our Focus
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Feature Representations
Height Weight Eye Color Gender
66 170 Blue Male
73 210 Brown Male
72 165 Green Male
70 180 Blue Male
74 185 Brown Male
68 155 Green Male
65 150 Blue Female
64 120 Brown Female
63 125 Green Female
67 140 Blue Female
68 165 Brown Female
66 130 Green Female
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Classification
• Identify which of N classes a data point, x, belongs to.
• x is a column vector of features.
OR
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Target Values
• In supervised approaches, in addition to a data point, x, we will also have access to a target value, t.
Goal of Classification
Identify a function y, such that y(x) = t
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Feature Representations
Height Weight Eye Color Gender
66 170 Blue Male
73 210 Brown Male
72 165 Green Male
70 180 Blue Male
74 185 Brown Male
68 155 Green Male
65 150 Blue Female
64 120 Brown Female
63 125 Green Female
67 140 Blue Female
68 165 Brown Female
66 130 Green Female
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Graphical Example of Classification
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Graphical Example of Classification
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Graphical Example of Classification
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Graphical Example of Classification
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Graphical Example of Classification
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Graphical Example of Classification
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Decision Boundaries
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Regression
• Regression is a supervised machine learning task. – So a target value, t, is given.
• Classification: nominal t • Regression: continuous t
Goal of Classification
Identify a function y, such that y(x) = t
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Differences between Classification and Regression
• Similar goals: Identify y(x) = t.• What are the differences?
– The form of the function, y (naturally).– Evaluation
• Root Mean Squared Error• Absolute Value Error• Classification Error• Maximum Likelihood
– Evaluation drives the optimization operation that learns the function, y.
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Graphical Example of Regression
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Graphical Example of Regression
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Graphical Example of Regression
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Clustering
• Clustering is an unsupervised learning task.– There is no target value to shoot for.
• Identify groups of “similar” data points, that are “dissimilar” from others.
• Partition the data into groups (clusters) that satisfy these constraints1. Points in the same cluster should be similar.
2. Points in different clusters should be dissimilar.
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Graphical Example of Clustering
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Graphical Example of Clustering
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Graphical Example of Clustering
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Mechanisms of Machine Learning
• Statistical Estimation– Numerical Optimization– Theoretical Optimization
• Feature Manipulation• Similarity Measures
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Mathematical Necessities
• Probability• Statistics• Calculus
– Vector Calculus
• Linear Algebra
• Is this a Math course in disguise?
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Why do we need so much math?
• Probability Density Functions allow the evaluation of how likely a data point is under a model. – Want to identify good PDFs. (calculus)– Want to evaluate against a known PDF. (algebra)
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Gaussian Distributions
• We use Gaussian Distributions all over the place.
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Gaussian Distributions
• We use Gaussian Distributions all over the place.
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Data Data Data
• “There’s no data like more data”• All machine learning techniques rely on the
availability of data to learn from.• There is an ever increasing amount of data being
generated, but it’s not always easy to process.– UCI
• http://archive.ics.uci.edu/ml/
– LDC (Linguistic Data Consortium)• http://www.ldc.upenn.edu/
– Contact me for speech data.
• Is all data equal?
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Class Structure and Policies
• Course website:– http://eniac.cs.qc.cuny.edu/andrew/ml/syllabus.html
• Email list– CUNY First has an email function – most students do not use
the associated email address…– Put your email address on the sign up sheet.