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. Introduction. Maximum Margin Classifiers. Support vector machines for linear classification.
Ensemble methods: bagging and boosting.
Summary & Highlights for Aa 19 20 Lecture 5
- Fuzzy sets and clustering. Fuzzy c-means. Manifold learning. Second assignment.
- Lazy learning. K-NN. Kernel regression and kernel density estimation.
- Supervised learning, minimization (least squares), polynomial regression.
- Probabilistic Clustering: mixture models. Expectation-Maximization revisited. Graphical methods, Hidden markov models.
- Hierarchical Clustering. Agglomerative and Divisive Clustering.
That wraps up our extensive overview of Aa 19 20 Lecture 5.
