Introduction to Machine Learning* Prof. D. Spears

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  • See pages 525-527 of the text for an explanation.
  • Introduction to Machine Learning* Prof. D. Spears

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

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