Introduction to Aa 19 20 Lecture 5
Let's dive into the details surrounding Aa 19 20 Lecture 5. Scoring classifiers. Cross-validation. Overfitting, model selection and regularization with logistic regression.
Aa 19 20 Lecture 5 Comprehensive Overview
Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions. Lazy learning. K-NN. Kernel regression and kernel density estimation. Introduction to deep learning.
Henry James.
Summary & Highlights for Aa 19 20 Lecture 5
- Introduction.
- Probabilistic Clustering: mixture models. Expectation-Maximization revisited. Graphical methods, Hidden markov models.
- Ensemble methods: bagging and boosting.
- In this
- Introduction to clustering. K-means and k-medoids. Expectation maximization.
That wraps up our extensive overview of Aa 19 20 Lecture 5.
