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Introduction to
Machine Learning*
Prof. D. Spears
COSC 4010/5010, Section 1
Spring 2004
* This material is taken from the textbook, Machine Learning, Volume I,
Eds. Michalski, Carbonell, and Mitchell, Tioga, 1983, and from Artificial
Intelligence by Russell and Norvig.
Definition of Machine Learning
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Informal definition: Any computer program that
improves its performance at some task through
experience and/or data.
Formal 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 E.
Wow! Look
at how much
it learned!
Other Disciplines From Which
Machine Learning Draws Ideas and
Techniques
decision
theory
AI
probability
&
statistics
biological
evolution
control
theory
information
theory
machine
learning
statistical
mechanics
computational
complexity
theory
ethology
game
theory
philosophy
optimization
psychology
neurophysiology
Some Learning
Strategies/Techniques
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Rote learning
Inductive inference
Stochastic/Bayesian inference
Deductive inference
Reinforcement learning
Neural network learning
Evolutionary learning
Clustering
Analogical learning
Learning from human instruction (being told)
Learning by discovery
Case-based reasoning
Speed-up learning
Multi-strategy learning is very popular
Examples of Types of Knowledge
Acquired Via Learning
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Declarative Knowledge
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Concepts
Preferred values of parameters
Grammars
Taxonomies
Procedural Knowledge
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Rules
Rule strengths
Graphs/networks
Computer programs
Plans
Example strategies
for acquisition:
Inductive inference
Evolutionary learning
Clustering
Analogy
Induction
Reinforcement learning
Evolutionary learning
Stochastic learning
Example Data Structures Used
for Learned Knowledge
Type of knowledge:
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Decision trees
Logical expressions
Neural networks
Condition-action rules
Rule sets
Finite-state automata
Lisp code
C code
Concepts
Behavioral rules
Plans
Computer programs
History of Machine Learning
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1950’s: Neural modeling
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1960’s: Pattern recognition and decision-theoretic learning
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E.g., perceptrons (Rosenblatt, 1958)
Groundwork for this work was laid by researchers in mathematical
biophysics (Rashevsky, 1948) (McCulloch and Pitts, 1943).
Major thrust was on learning tabula rasa. Focus on self-organization
and neuron-like learning elements.
Acquire linear, polynomial, or related forms of a discriminant function
from a given set of training examples, e.g., (Nilsson, 1965).
Samuel’s checker’s program (Samuel, 1959, 1963). Acquired a master
level of performance.
Statistical decision theory for pattern recognition, e.g., (Watanabe,
1960) (Duda & Hart, 1973).
1969: Minsky & Papert on theoretical limitations of perceptron
learning.
1970s: Adaptive control
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Self-adjust parameters to maintain stability in spite of disturbances, e.g.,
(Davies, 1970) (Fu, 1971).
History of Machine Learning
(cont’d)
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1960’s and 70’s: Models of human learning
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1970’s: Genetic algorithms
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High-level symbolic descriptions of knowledge, e.g., logical expressions
or graphs/networks, e.g., (Karpinski & Michalski, 1966) (Simon & Lea,
1974).
META-DENDRAL (Buchanan, 1978), for example, acquired task-specific
expertise (for mass spectrometry) in the context of an expert system.
Winston’s (1975) structural learning system learned logic-based
structural descriptions from examples.
Developed by Holland (1975)
1970’s - present: Knowledge-intensive learning
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A tabula rasa approach typically fares poorly. “To acquire new
knowledge a system must already possess a great deal of initial
knowledge.” Lenat’s CYC project is a good example.
History of Machine Learning
(cont’d)
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1970’s - present: Alternative modes of learning (besides examples)
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Learning from instruction, e.g., (Mostow, 1983) (Gordon & Subramanian,
1993)
Learning by analogy, e.g., (Veloso, 1990)
Learning from cases, e.g., (Aha, 1991)
Discovery (Lenat, 1977)
1991: The first of a series of workshops on Multistrategy Learning (Michalski)
1970’s – present: Meta-learning
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Heuristics for focusing attention, e.g., (Gordon & Subramanian, 1996)
Active selection of examples for learning, e.g., (Angluin, 1987), (Gasarch &
Smith, 1988), (Gordon, 1991)
Learning how to learn, e.g., (Schmidhuber, 1996)
History of Machine Learning
(cont’d)
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1980 – The First Machine Learning Workshop was held at Carnegie-Mellon
University in Pittsburgh.
1980 – Three consecutive issues of the International Journal of Policy
Analysis and Information Systems were specially devoted to machine
learning.
1981 – A special issue of SIGART Newsletter reviewed current projects in
the field of machine learning.
1983 – The Second International Workshop on Machine Learning, in
Monticello at the University of Illinois.
1986 – The establishment of the Machine Learning journal.
1987 – The beginning of annual international conferences on machine
learning (ICML).
1988 – The beginning of regular workshops on computational learning
theory (COLT).
1990’s – Explosive growth in the field of data mining, which involves the
application of machine learning techniques.
A general model of
external
learning
agents
performance
standard
critic
sensors
feedback
learning
element
changes
performance
knowledge element
learning goals
problem
generator
effectors
AGENT
environment
Evaluating Learners
on unseen data
Learning curves
A
C
C
U
R
A
C
Y
AMOUNT OF TRAINING DATA SEEN
Some Ideas for Projects
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Multi-agent / swarm reinforcement learning
Concept learning using logical, stochastic, neural, or evolutionary
representations or hybrids
Learning a good representation for learning concepts (meta-learning)
Data mining: Discovering patterns in large data sets (medical? consumer?)
Modeling the process of scientific discovery
Evolving a simple artificial brain
Cognitive models of human learning
“Safe” learning
Learning in artificial life/worlds
Learning in soccer-playing agents
Unsupervised learning (clustering) to develop taxonomies
Learning to predict temporal sequences
Training a neural network to recognize objects, faces, etc.
Multi-agent learning to cooperate or compete
Learning to improve game playing strategies
Evolving computer programs (genetic programming)
Comparative studies of different learning methods
A variant of a study found in a machine learning conference paper
Analogical learning (e.g., applying knowledge of one case to a new case)
Learning a model of a student for intelligent tutoring