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This document provides an overview of Bayesian learning and instance-based learning methods in machine learning. Bayesian learning is fundamentally based on probabilistic reasoning, utilizing Bayes' Theorem to calculate the posterior probability of a hypothesis given observed training data. Key concepts include the Maximum A Posteriori (MAP) hypothesis, the Maximum Likelihood (ML) hypothesis, and the Bayes Optimal Classifier. The Naive Bayes classifier is a practical application that simplifies probability estimation by assuming conditional independence of attributes.
The document also explores instance-based, or "lazy," learning methods, such as K-Nearest Neighbor (KNN) and Locally Weighted Regression (LWR). These methods postpone generalization until a new instance needs to be classified. Instance-based methods offer flexibility by estimating the target function locally for each new instance. Finally, the text touches on Bayesian Belief Networks (BBNs) and the Expectation-Maximization (EM) algorithm for learning with unobserved variables.
Key Topics Covered:
The document also explores instance-based, or "lazy," learning methods, such as K-Nearest Neighbor (KNN) and Locally Weighted Regression (LWR). These methods postpone generalization until a new instance needs to be classified. Instance-based methods offer flexibility by estimating the target function locally for each new instance. Finally, the text touches on Bayesian Belief Networks (BBNs) and the Expectation-Maximization (EM) algorithm for learning with unobserved variables.
Key Topics Covered:
- Bayes Theorem: A foundational formula that allows the calculation of the posterior probability of a hypothesis ($P(h|D)$) from its prior probability ($P(h)$), the probability of the data given the hypothesis ($P(D|h)$), and the probability of the data ($P(D)$).
- Maximum A Posteriori (MAP) Hypothesis: The most probable hypothesis ($h_{MAP}$) from the hypothesis space $H$, given the observed training data $D$.
- Naive Bayes Classifier: A highly practical Bayesian learning method that simplifies probability estimation by assuming that the attribute values are conditionally independent given the target value.
- K-Nearest Neighbor (KNN) Learning: A fundamental instance-based method that classifies a new instance based on the most common target value (for discrete functions) or the mean value (for real-valued functions) among its $K$ nearest training examples.
- Lazy Learning Methods: A category of instance-based learning algorithms, such as KNN and Locally Weighted Regression (LWR), that store the training examples and delay the generalization process until a new instance is queried for classification.
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