AIMA: Added key terms and section headers from 20.1 and 20.2

OMSCS/Courses/AI/AIMA Textbook Notes/AIMA - Chapter 20 - Learning Probabilistic Models.md

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+---
+tags:
+  - OMSCS
+  - AI
+  - AIMA
+  - ML
+---
+# AIMA - Chapter 20 - Learning Probabilistic Models
+
+## 20.1 Statistical Learning
+Key terms and section headers
+- data
+- hypotheses
+- evidence
+- bayesian learning
+- hypothesis prior
+- likelihood
+- maximum a posteriori (MAP, pronounced M-A-P)
+- minimum description length (MDL)
+- uniform
+- maximum-likelihood hypothesis ($h_{ML}$)
+
+## 20.2 Learning with Complete Data
+key terms and section headers
+- density estimation
+- complete data
+- parameter learning
+- maximum-likelihood parameter learning : discrete models
+	- log likelihood
+- Naive Bayes Models
+- generative and discriminative models
+	- generative model
+	- discriminative model
+- maximum-likelihood parameter learning : continuous models
+	- linear-Gaussian
+	- linear regression
+- Bayesian parameter learning
+	- beta distributions
+	- hyperparameters
+	- virtual counts
+	- parameter independence
+- Bayesian linear regression
+	- uninformative prior
+- learning Bayes net structures
+- Density estimation with nonparametric models
+	- nonparametric density estimation
+	- k-nearest-neighbors
+	- kernel functions
+
+## 20.3 Learning with Hidden Variables: The EM Algorithm
+(skipped for now)