Understanding Aa 18 19 Lecture 6
Exploring Aa 18 19 Lecture 6 reveals several interesting facts. Lazy learning. K-NN. Kernel regression and kernel density estimation.
Key Takeaways about Aa 18 19 Lecture 6
- Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions.
- Introduction.
- Graphical methods, Hidden markov models. The Baum-Welch and Vitterbi algorithms.
- Supervised learning, minimization (least squares), polynomial regression.
- Lazy learning. K-NN. Kernel regression and kernel density estimation.
Detailed Analysis of Aa 18 19 Lecture 6
Hierarchical Clustering. Agglomerative and Divisive Clustering. Clustering Features. Generative models: naive bayes, bayes. Comparing classifiers. Assignment 1. Dimensionality reduction: feature extraction with PCA; self-organzing maps.
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