Download CMPUT466/551 Machine Learning

Machine Learning
CMPUT 466/551
Nilanjan Ray
Department of Computing Science
University of Alberta
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Introduction
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What is machine learning (ML)?
Taxonomy in ML
Applications/Examples
Related disciplines
References
Resources
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What is machine learning (ML)?
• Definition of “learning” from Merriam-Webster:
“To gain knowledge or understanding of or skill
in by study, instruction, or experience”
• ML = Learning in machines (computers)
• ML techniques are algorithms that enable the
machines to improve its performance at some
task through experience
• Tasks: recognition, diagnosis, prediction,
planning, data mining, robot control, and so on.
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Why Machine Learning?
• Some tasks can be specified only by training
data/examples
• Human expertise may be scarce and/or very
costly
• Amount of knowledge might be too large for
explicit encoding by humans
• Modeling/Hidden parameter estimation: Often
only data from measurements are available
• Computational power is ever increasing
• Growing data pool and storage capacity
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A Brief History about ML
• 1950’s:
– Samuel’s checker program
– Rosenblatt’s perceptron
• 1960’s:
– Neural network
– Pattern recognition
• 1970’s:
– Winston’s ARCH
– Buchanan and Mitchell’s Meta-Dendral: mass spectrometry
prediction rules
– Quinlan’s ID3: Chess end-game rules
– Michalski’s AQ11: Soybean disease diagnosis rules
– MACROPS: macro operators in block world planning
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A Brief History about ML…
• 1980’s: Gains momentum
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Learning theory
Symbolic learning algorithms
Connectionist learning algorithm
Clustering
Explanation-based learning
Knowledge guided inductive learning
Genetic algorithm
• 1990’s: Maturity
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Data mining
Ensemble learning: bagging, boosting etc.
Kernel methods
Reinforcement learning
Theoretical analysis
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Typical Taxonomy for ML
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Supervised learning
Unsupervised learning
Semi-supervised learning
Reinforcement learning
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Supervised Learning
• Training data available in the form of
(input, output) pairs
• When output is continuous valued the
problem is called regression; if the output
is qualitative or categorical the problem is
called classification
• The goal here is to estimate the output for
a novel (never-seen-before) input, after
learning the training data
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A Supervised Learning Example
(From [DHS] book)
• “Sorting incoming Fish on a conveyor
according to species using optical
sensing”
Sea bass
Species
Salmon
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Problem Analysis
• Set up a camera and take some sample
images to extract features
– Length
– Lightness
– Width
– Number and shape of fins
– Position of the mouth, etc…
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Preprocessing
• Use a segmentation operation to isolate fishes
from one another and from the background
• Information from a single fish is sent to a
feature extractor whose purpose is to reduce
the data by measuring certain features
• The features are passed to a classifier
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Feature for Classification
Can we select the length of the fish as a possible
feature for discrimination?
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Let’s Try Another Feature
Lightness (Intensity of image pixels)
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Yet Another Feature: Width
• Adopt the lightness and add the width of
the fish
Fish
xT = [x1, x2]
Lightness
Width
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A Classifier
So, how does the “Width” feature help?
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Another Classifier
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Unsupervised Learning
• No training data in the form of (input, output) pair
is available
• Applications:
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Dimensionality reduction
Data compression
Outlier detection
Classification
Segmentation/clustering
Probability density estimation
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Example: Unsupervised Learning
DNA microarray data (taken from [HTF])
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Example: Unsupervised
Learning (contd..)
• Applications on DNA microarray
– Clustering: Group genes or samples into similar
expression profiles
– Bi-clustering: Subset of genes exhibiting similar
expression pattern along a subset of samples
– Dimension reduction
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Semi-supervised Learning
• Uses both labeled data (in the form (input,
output) pairs) and unlabelled data for
learning
• When labeling of data is a costly affair
semi-supervised techniques could be very
useful
• Examples: Generative models, selftraining, co-training
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Example: Semi-supervised Learning
Source: Semi-supervised literature survey by X. Zhu, Technical Report
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Reinforcement Learning
• Reinforcement learning is the problem faced by an agent
that must learn behavior through trial-and-error
interactions with a dynamic environment.
• There is no teacher telling the agent wrong or right
• There is critic that gives a reward / penalty for the
agent’s action
• Applications:
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Robotics
Combinatorial search problems, such as games
Industrial manufacturing
Many others!
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Example: Reinforcement Learning
Tic Tac Toe
TD-Gammon
Goal
Learn to play optimal
game
Learn to play game
at master level
States
All possible board
states - 9
All possible board
states - 1020
Action
A new X in an empty
field
21 dice combinations
& avg. 20 legal
moves
Reinforcement
Signal
+10 winning
-1 for every move
that did not win
‘1 ‘ for a reward
‘0‘ for a penalty
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Related Disciplines
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Statistics
Artificial Intelligence
Psychology
Vision and Neuroscience
Control Theory
Signal and Image Processing
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References and Journals
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Text: The Elements of Statistical Learning by Hastie, Tibshirani, and
Friedman (book website: http://www-stat.stanford.edu/~tibs/ElemStatLearn/)
Reference books:
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Pattern Classification by Duda, Hart and Stork
Pattern Recognition and Machine Learning by C.M. Bishop
Machine Learning by T. Mitchell
Introduction to Machine Learning by E. Alpaydin
Some related journals / associations:
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Machine Learning (Kluwer).
Journal of Machine Learning Research.
Journal of AI Research (JAIR).
Data Mining and Knowledge Discovery - An International Journal.
Journal of Experimental and Theoretical Artificial Intelligence (JETAI).
Evolutionary Computation.
Artificial Life.
Fuzzy Sets and Systems
IEEE Intelligent Systems (Formerly IEEE Expert)
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Systems, Man and Cybernetics
Journal of AI Research
Journal of Intelligent Information Systems
Journal of the American Statistical Association
Journal of the Royal Statistical Society
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References and Journals…
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Pattern Recognition
Pattern Recognition Letters
Pattern Analysis and Applications.
Computational Intelligence .
Journal of Intelligent Systems .
Annals of Mathematics and Artificial Intelligence.
IDEAL, the online scientific journal library by Academic Press.
ECCAI (European Coordinating Committee on Artificial Intelligence).
AAAI (American Association for Artificial Intelligence).
IJCAI (International Joint Conferences on Artificial Intelligence, Inc.).
ACM (Association for Computing Machinery).
Association for Uncertainty in Artificial Intelligence.
ACM SIGAR
ACM SIGMOD
American Statistical Association.
Artificial Intelligence
Artificial Intelligence in Engineering
Artificial Intelligence in Medicine
Artificial Intelligence Review
Bioinformatics
Data and Knowledge Engineering
Evolutionary Computation
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Some Conferences & Workshops
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Congress on Evolutionary Computation
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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery
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The ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
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National Conference on Artificial Intelligence
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Genetic and Evolutionary Computation Conference
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International Conference on Machine Learning
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Conference on Autonomous Agents and Multiagent Systems
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European Symposium on Artificial Neural Networks Advances in Computational Intelligence and
Learning
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Artificial and Ambient Intelligence
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Computational Intelligene in Biomedical Engineering
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IEEE International Symposium on Approximate Dynamic Programming and Reinforcement
Learning
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International Joint Conference on Artificial Intelligence
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