Introduction to Aa 17 18 Lecture 8
Let's dive into the details surrounding Aa 17 18 Lecture 8. Perceptron and Multilayer Perceptron.
Aa 17 18 Lecture 8 Comprehensive Overview
Maximum Margin Classifiers. Support vector machines for linear classification. Generative models: naive bayes, bayes. Comparing classifiers. Assignment 1. Scoring classifiers. Cross-validation. Overfitting, model selection and regularization with logistic regression.
Summary & Highlights for Aa 17 18 Lecture 8
- Bayesian Decision theory. Maximum a posteriori estimation. Decisions and costs.
- Lazy learning. K-NN. Kernel regression and kernel density estimation.
- Graphical methods, Hidden markov models. The Baum-Welch and Vitterbi algorithms.
- Professor Beverly Gage begins her
That wraps up our extensive overview of Aa 17 18 Lecture 8.
