Exploring Aa 17 18 Lecture 3
Let's dive into the details surrounding Aa 17 18 Lecture 3.
- 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.
- Scoring classifiers. Cross-validation. Overfitting, model selection and regularization with logistic regression.
- Professor Beverly Gage begins her 8 classes for the final portion of the course with issues surrounding immigration. Recorded in ...
In-Depth Information on Aa 17 18 Lecture 3
Overfitting and regularization with polynomial regression. Select models: Train, validate, test. Empirical Risk Minimization. Decision theory. Probably Approximately Correct Learning. VC dimension and shattering. Introduction to clustering. K-means and k-medoids. Expectation maximization. Bayesian Decision theory. Maximum a posteriori estimation. Decisions and costs.
Affinity Propagation clustering and problems with prototype-based clustering. Density Clustering. Clustering validation.
That wraps up our extensive overview of Aa 17 18 Lecture 3.