Machine Learning according to Cyberiad

by Andreas (Andrzej) Wichert

1. Introduction

2. Decision Trees

3. Probability theory & Information

4. Linear Algebra & Optimization

5. Linear Regression & Bayesian Linear Regression

6. Perceptron & Logistic Regression

7. Multilayer Perceptrons

8. Learning theory, Bias-Variance

9. K-Means, EM-Clustering

10. Kernel Methods & RBF

11. Support Vector Machines

12. Model Selection

13. Deep Learning

14. Convolutional Neural Networks

15. Recurrent Neural Networks

16. PCA, ICA

17. Autoencoders

18. Feature Extraction

19. k Nearest Neighbour & Locally Weighted Regression

20. Ensemble Methods

21. Bayesian Networks

22. Stochastic Methods