Introduction to Aa 17 18 Lecture 5
Exploring Aa 17 18 Lecture 5 reveals several interesting facts. Scoring classifiers. Cross-validation. Overfitting, model selection and regularization with logistic regression.
Aa 17 18 Lecture 5 Comprehensive Overview
Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions. Graphical methods, Hidden markov models. The Baum-Welch and Vitterbi algorithms. Generative models: naive bayes, bayes. Comparing classifiers. Assignment 1.
Summary & Highlights for Aa 17 18 Lecture 5
- Overfitting and regularization with polynomial regression. Select models: Train, validate, test.
- Introduction.
- Supervised learning, minimization (least squares), polynomial regression.
- Lazy learning. K-NN. Kernel regression and kernel density estimation.
- Ensemble methods: bagging and boosting.
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