Exploring Aa 19 20 Lecture 7
Let's dive into the details surrounding Aa 19 20 Lecture 7.
- Introduction.
- Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions.
- Hierarchical Clustering. Agglomerative and Divisive Clustering.
- Supervised learning, minimization (least squares), polynomial regression.
- ➡️A heatwave is hitting Europe and the US recently ➡️Besides increasing the number of people suffering from heat-related ...
In-Depth Information on Aa 19 20 Lecture 7
Generative models: naive bayes, bayes. Comparing classifiers. Introduction to clustering. K-means and k-medoids. Expectation maximization. Maximum Margin Classifiers. Support vector machines for linear classification. Fuzzy sets and clustering. Fuzzy c-means. Manifold learning. Second assignment.
Ensemble methods: bagging and boosting.
That wraps up our extensive overview of Aa 19 20 Lecture 7.