Introduction to Aa 17 18 Lecture 6
If you are looking for information about Aa 17 18 Lecture 6, you have come to the right place. Lazy learning. K-NN. Kernel regression and kernel density estimation.
Aa 17 18 Lecture 6 Comprehensive Overview
Perceptron and Multilayer Perceptron. Professor Beverly Gage begins her 8 classes for the final portion of the course with issues surrounding immigration. Recorded in ... Generative models: naive bayes, bayes. Comparing classifiers. Assignment 1.
Ensemble methods: bagging and boosting.
Summary & Highlights for Aa 17 18 Lecture 6
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- Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions.
- Graphical methods, Hidden markov models. The Baum-Welch and Vitterbi algorithms.
- Scoring classifiers. Cross-validation. Overfitting, model selection and regularization with logistic regression.
- Overfitting and regularization with polynomial regression. Select models: Train, validate, test.
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