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. For sponsors, please contact ahmeddhia1979@gmail.com If you watched the Maximum Margin Classifiers. Support vector machines for linear classification.
Supervised learning, minimization (least squares), polynomial regression.
Summary & Highlights for Aa 19 20 Lecture 5
- Lazy learning. K-NN. Kernel regression and kernel density estimation.
- Introduction.
- Probabilistic Clustering: mixture models. Expectation-Maximization revisited. Graphical methods, Hidden markov models.
- Hierarchical Clustering. Agglomerative and Divisive Clustering.
- Lect 19 20 Q5
That wraps up our extensive overview of Aa 19 20 Lecture 5.