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  • 1. Machine Learning CSE 4308/5360 – Artificial Intelligence I Darin Brezeale The University of Texas at Arlington Machine Learning – p. 1/2
  • 2. Introduction Machine learning is the area of artificial intelligence in which we ‘teach’ the computer what it knows. This is typically done by providing training examples, either labeled (supervised learning) or unlabeled (unsupervised learning). Many different methods have been developed for performing machine learning, each with its strengths and weaknesses. Machine Learning – p. 2/2
  • 3. Introduction Why learn instead of just hard-coding rules? • It may not be practical; the number of rules may be too large. • Humans may not understand the domain well-enough to write the rules, e.g., in recognizing faces. • Things change; we would like a program to be able to change as well without much human intervention. Machine Learning – p. 3/2
  • 4. Data Representation A question that is often neglected when discussing machine learning is ‘How do I represent my training examples?’ A typical approach is to represent the examples as vectors. EX: If we had samples for various animals, then the sample <dog, has hair, barks, flys, tail, no gills> might become {dog, 1, 1, 0, 1, 0} Machine Learning – p. 4/2
  • 5. Neural Network A neural network (or artificial neural network) is a network of simple units. The artificial neural network is inspired by the network of neurons in the human brain. Neural networks can handle discrete or real-valued functions and have been very popular in various pattern recognition applications, e.g., recognizing faces. Machine Learning – p. 5/2
  • 6. Neural Network Figure 1: Simple multilayer feedforward network. Machine Learning – p. 6/2
  • 7. Neural Network The value of a unit is a weighted sum of its input. Figure 2: Relationship between a single unit and its input units. Machine Learning – p. 7/2
  • 8. Neural Network The big questions when using a neural network are: 1. What type of network (e.g., feedforward, back-propagation, etc.) should be used? 2. What should the network topology (i.e., how many hidden nodes) be? Machine Learning – p. 8/2
  • 9. Neural Network Pros: • can fit nonlinear functions • can handle noisy data Cons: • hard to understand the resulting function weights • can take a long time to train Machine Learning – p. 9/2
  • 10. Genetic Algorithm Genetic algorithms are based on the concept of natural selection with the attributes of the samples treated as ‘chromosomes’. They are useful for solving optimization problems. We don’t train the system; instead, we are seeking the combination of values that produces the best result. Machine Learning – p. 10/2
  • 11. Genetic Algorithm The various solutions to a problem are individuals in a population. Some produce better function values than others. The stronger individuals are combined to produce new offspring with operations such as crossover and mutation. Machine Learning – p. 11/2
  • 12. Genetic Algorithm Figure 3: Application of crossover. Machine Learning – p. 12/2
  • 13. Genetic Algorithm Pros: • Domain independent. • Can overcome local optima. Cons: • Not always clear how to represent the problem. • Must choose fitness function, rate of cross-over, rate of mutation, etc. Machine Learning – p. 13/2
  • 14. Decision Tree Decision trees are trees in which the branches represent the values of attributes. They can be used to represent discrete-valued functions. Machine Learning – p. 14/2
  • 15. Decision Tree Figure 4: Simplistic decision tree for classifying some types of animals. Machine Learning – p. 15/2
  • 16. Decision Tree The big question when constructing decision trees is the order of attributes to branch on. Often the attributes are ordered by the information gain from them. Example: If we constructed a decision tree to classify mammals as dog or not-dog, the top split should not be ‘walks’ since most mammals walk. A better choice might be ‘barks’. Machine Learning – p. 16/2
  • 17. Decision Tree Pros: easy to understand results Cons: prone to overfitting (pruning can address this) Machine Learning – p. 17/2
  • 18. Reinforcement Learning In reinforcement learning, the agent receives feedback from its actions and seeks to maximize its total reward. This is different from supervised learning in that training examples are not provided. Used for sequential decisions. See examples at: http://www.cs.ualberta.ca/ sutton/book/ebook/node8.html Machine Learning – p. 18/2
  • 19. More Recent Methods The methods previously described are some of the oldest. More recent methods are: • statistics-based – actually, these have been around for a while, but they remain the focus of much research, especially as statisticians begin looking at machine learning and data mining • matrix decompositions – learn a basis for each category; classify as category whose basis vectors best represent the new sample. Machine Learning – p. 19/2
  • 20. References • Hastie, Trevor, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning, 2nd ed, Springer, 2009. • Marczyk, Adam, Genetic Algorithms and Evolutionary Computation, URL: http://www.talkorigins.org/faqs/genalg/genalg.html, accessed: August 9, 2009. • Mitchell, Tom M., Machine Learning, McGraw-Hill, 1997. • Moore, Andrew W., Statistical Data Mining Tutorials, URL: http://www.autonlab.org/tutorials/, accessed: August 9, 2009. • Sutton, Richard S. and Andrew G. Barto, Reinforcement Learning: An Introduction, The MIT Press, 1998. Machine Learning – p. 20/2