Exploring Aa 17 18 Lecture 3
Let's dive into the details surrounding Aa 17 18 Lecture 3.
- Introduction.
- Introduction to clustering. K-means and k-medoids. Expectation maximization.
- Graphical methods, Hidden markov models. The Baum-Welch and Vitterbi algorithms.
- The ages of
- Supervised learning, minimization (least squares), polynomial regression.
In-Depth Information on Aa 17 18 Lecture 3
Overfitting and regularization with polynomial regression. Select models: Train, validate, test. Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions. Scoring classifiers. Cross-validation. Overfitting, model selection and regularization with logistic regression. Empirical Risk Minimization. Decision theory. Probably Approximately Correct Learning. VC dimension and shattering.
Bayesian Decision theory. Maximum a posteriori estimation. Decisions and costs.
That wraps up our extensive overview of Aa 17 18 Lecture 3.
