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Machine Learning Methods

Machine Learning Methods. Maximum entropy Maxent is an example Boosting : Boosted Regression Trees Neural Networks. Machine Learning. Branch of artificial intelligence Supervised learning: Algorithms that “learn” from data Deals with representation and generalization Generalization:

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Machine Learning Methods

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  1. Machine Learning Methods • Maximum entropy • Maxent is an example • Boosting: • Boosted Regression Trees • Neural Networks

  2. Machine Learning • Branch of artificial intelligence • Supervised learning: • Algorithms that “learn” from data • Deals with representation and generalization • Generalization: • Can operate on data that has not been seen before • Rigor provided by computational learning theory

  3. Issues • Methods we’ll examine: • Work just like linear regression but can produce much more complex models • Fitting algorithms are “hidden” • Parameters harder to access and examine • Produce better “model fits” • Tend to “over fit”

  4. Tamarisk: Temp and Precip 162 parameters

  5. Boosting • Using “weak learners” together to make a “stronger learner”. • Gradient boosting • For regression problems • Uses an “ensemble” of weak prediction models • Gradient tree boosting • Uses many tiny regression trees to make more complex models

  6. Boosted Regression Trees • The “weak” learners are individual, binary trees of three nodes • There can be thousands of trees! • The trees are hidden within the model • Now we can really over fit our model!

  7. Boosted Regression Trees Relative dominance black-spruce vs. deciduous trees in post-fire Alaska http://www.lter.uaf.edu/bnz_disturbance.cfm

  8. Brown Shrimp BRT Model

  9. A Neuron Wikipedia

  10. Neural Network • Basically: • Neurons “sum” charge from other neurons • When charge goes over a threshold, • The neuron turns “on” and sends a signal to other neurons

  11. Artificial neural network AIDA

  12. Artificial Neural Networks • Advantages: • Very flexible • Can model “fuzzy” problems • Successes in simple visual recognition, some expert systems. • Disadvantages: • Hidden model • Can be slow • Have not been able to solve a wide range of problems

  13. Expert Systems • Attempt to capture “expertise” • Originally was part of the promise of neural networks • Now largely driven off very large databases • WebMD was one attempt • Ask Jeeves was another (ask.com)

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