IT/CS 811 Principles of

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IT/CS 811 Principles of Machine Learning and
Inference
1. Introduction
Prof. Gheorghe Tecuci
Learning Agents Laboratory Computer Science
Department George Mason University
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Overview
What is Artificial Intelligence
What is Machine Learning
History of Machine Learning
Basic bibliography and reading
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What is Artificial Intelligence
Artificial Intelligence is the Science and
Engineering that is concerned with the theory and
practice of developing systems that exhibit the
characteristics we associate with intelligence in
human behavior perception, natural language
processing, reasoning, planning and problem
solving, learning and adaptation, etc.
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Central goals of Artificial Intelligence
Understand the principles that make intelligence
possible(in humans, animals, and artificial
agents)
Developing intelligent machines or agents(no
matter whether they operate as humans or not)
Formalizing knowledge and mechanizing
reasoningin all areas of human endeavor
Making the working with computers as easy as
working with people
Developing human-machine systems that exploit the
complementariness of human and automated
reasoning
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What is an intelligent agent

• An intelligent agent is a system that
• perceives its environment (which may be the
  physical world, a user via a graphical user
  interface, a collection of other agents, the
  Internet, or other complex environment)
• reasons to interpret perceptions, draw
  inferences, solve problems, and determine
  actions and
• acts upon that environment to realize a set of
  goals or tasks for which it was designed.

input/
sensors
IntelligentAgent
output/
user/ environment
effectors
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Characteristic features of intelligent agents
Knowledge representation and reasoning
Transparency and explanations
Ability to communicate
Use of huge amounts of knowledge
Exploration of huge search spaces
Use of heuristics
Reasoning with incomplete or conflicting data
Ability to learn and adapt
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Overview
What is Artificial Intelligence
What is Machine Learning
History of Machine Learning
Basic bibliography and reading
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What is Machine Learning
Machine Learning is the domain of Artificial
Intelligence which is concerned with building
adaptive computer systems that are able to
improve their competence and/or efficiency
through learning from input data or from their
own problem solving experience.
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The architecture of a learning agent
Implements a general problem solving method that
uses the knowledge from the knowledge base to
interpret the input and provide an appropriate
output.
Implements learning methods for extending and
refining the knowledge base to improve agents
competence and/or efficiency in problem solving.
Learning Agent
Problem Solving Engine
Input/
Sensors
Learning Engine
User/ Environment
Output/
Ontology Rules/Cases/Methods
Knowledge Base
Effectors
Data structures that represent the objects from
the application domain, general laws governing
them, actions that can be performed with them,
etc.
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What is Learning?
Learning denotes changes in the system that are
adaptive in the sense that they enable the system
to do the same task or tasks drawn from the same
population more effectively the next time (Simon,
1983).
Learning is making useful changes in our minds
(Minsky, 1985).
Learning is constructing or modifying
representations of what is being experienced
(Michalski, 1986).
A computer program learns if it improves its
performance at some task through experience
(Mitchell, 1997).
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So what is Learning?

Learning is a very general term denoting the way
in which people and computers

• acquire and organize knowledge (by building,
  modifying and organizing internal representations
  of some external reality)
• discover new knowledge and theories (by creating
  hypotheses that explain some data or phenomena)
• acquire skills (by gradually improving their
  motor or cognitive skills through repeated
  practice, sometimes involving little or no
  conscious thought).

Learning results in changes in the agent (or
mind) that improve its competence and/or
efficiency.
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Two complementary dimensions for learning
Competence
A system is improving its competence if it learns
to solve a broader class of problems, and to make
fewer mistakes in problem solving.
Efficiency
A system is improving its efficiency, if it
learns to solve the problems from its area of
competence faster or by using fewer resources.
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Main directions of research in Machine Learning
Discovery of general principles, methods,and
algorithms of learning
Automation of the constructionof knowledge-based
systems
Modeling human learning mechanisms
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Learning strategies
A Learning Strategy is a basic form of learning
characterized by the employment of a certain type
of inference (like deduction, induction or
analogy) and a certain type of computational or
representational mechanism (like rules, trees,
neural networks, etc.).

• Instance-based learning
• Reinforcement learning
• Neural networks
• Genetic algorithms and evolutionary
  computation
• Reinforcement learning
• Bayesian learning
• Multistrategy learning

• Rote learning
• Learning from instruction
• Learning from examples
• Explanation-based learning
• Conceptual clustering
• Quantitative discovery
• Abductive learning
• Learning by analogy

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Overview
What is Artificial Intelligence
What is Machine Learning
History of Machine Learning
Basic bibliography and reading
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History of Machine Learning

• Early enthusiasm (1955 - 1965)
• Learning without knowledge
• Neural modeling (self-organizing systems and
  decision space techniques)
• Evolutionary learning
• Rote learning (Samuel Checkers player).

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History of Machine Learning (cont.)

• Dark ages (1962 - 1976)
• To acquire knowledge one needs knowledge
• Realization of the difficulty of the learning
  process and of the limitations of the explored
  methods (e.g. the perceptron cannot learn the XOR
  function)
• Symbolic concept learning (Winstons influential
  thesis, 1972).

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History of Machine Learning (cont.)

• Renaissance (1976 - 1988)
• Exploration of different strategies (EBL, CBR,
  GA, NN, Abduction, Analogy, etc.)
• Knowledge-intensive learning
• Successful applications
• Machine Learning conferences/workshops worldwide.

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History of Machine Learning (cont.)

• Maturity (1988 - present)
• Experimental comparisons
• Revival of non-symbolic methods
• Computational learning theory
• Multistrategy learning
• Integration of machine learning and knowledge
  acquisition
• Emphasis on practical applications.

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Successful applications of Machine Learning

• Learning to recognize spoken words (all of the
  most successful systems use machine learning)
• Learning to drive an autonomous vehicle on public
  highway
• Learning to classify new astronomical structures
  (by learning regularities in a very large data
  base of image data)
• Learning to play games
• Automation of knowledge acquisition from domain
  experts
• Learning agents.

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Basic bibliography
Mitchell T.M., Machine Learning, McGraw Hill,
1997. Shavlik J.W. and Dietterich T. (Eds.),
Readings in Machine Learning, Morgan Kaufmann,
1990. Buchanan B., Wilkins D. (Eds.), Readings in
Knowledge Acquisition and Learning Automating
the Construction and the Improvement of Programs,
Morgan Kaufmann, 1992. Langley P., Elements of
Machine Learning, Morgan Kaufmann,
1996. Michalski R.S., Carbonell J.G., Mitchell
T.M. (Eds), Machine Learning An Artificial
Intelligence Approach, Morgan Kaufmann, 1983
(Vol. 1), 1986 (Vol. 2). Kodratoff Y. and
Michalski R.S. (Eds.) Machine Learning An
Artificial Intelligence Approach (Vol. 3), Morgan
Kaufmann Publishers, Inc., 1990. Michalski R.S.
and Tecuci G. (Eds.), Machine Learning A
Multistrategy Approach (Vol. 4), Morgan Kaufmann
Publishers, San Mateo, CA, 1994. Tecuci G. and
Kodratoff Y. (Eds.), Machine Learning and
Knowledge Acquisition Integrated Approaches,
Academic Press, 1995. Tecuci G., Building
Intelligent Agents An Apprenticeship
Multistrategy Learning Theory, Methodology, Tool
and Case Studies, Academic Press, 1998.
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Recommended reading
Mitchell T.M., Machine Learning, Chapter 1
Introduction, pp. 1-19, McGraw Hill, 1997.