1. Machine learning uses algorithms to learn patterns from labeled data samples represented as feature vectors. Models like neural networks, support vector machines, decision trees, and boosting algorithms are used to classify patterns. 2. Neural networks use layers of nodes and backward propagation of errors to learn weights, while support vector machines find an optimal separating boundary between classes. Decision trees use feature testing and impurity measures to split data into branches. 3. Bayesian approaches apply probability densities and maximum entropy principles to compute posterior distributions over classes given observed data and features.

1. Machine Learning: An Introduction Fu Chang Institute of Information Science Academia Sinica 2788-3799 ext. 1819 [email_address]

5. Patterns as Conglomerations of sample Points A B Two types of sample points

9. Neural Networks

11. Illustration

13. Support Vector Machines (SVM)

15. Illustration

16. Classification and Regression Tree (CART)

17. Illustration

20. AdaBoost

22. Boltzmann-Gibbs Model