Exploring Aa 19 20 Lecture 10
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- Perceptron and Multilayer Perceptron.
- Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions.
- Introduction to clustering. K-means and k-medoids. Expectation maximization.
- Art of Problem Solving's Richard Rusczyk solves 2015 AMC
- Tonight on **The Black Line Brief**, we're breaking down **THE FALLOUT** from the Candace Owens vs. Andrew Wilson ...
In-Depth Information on Aa 19 20 Lecture 10
SVM: soft margins, kernel trick, overfitting and regularization. Assignment 1. Empirical Risk Minimization. Decision theory. Probably Approximately Correct Learning. VC dimension and shattering. Maximum Margin Classifiers. Support vector machines for linear classification. Generative models: naive bayes, bayes. Comparing classifiers.
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In summary, understanding Aa 19 20 Lecture 10 gives us a better perspective.
