Machine Learning according to Cyberiad


by Andreas (Andrzej) Wichert


1. Introduction

2. Probability theory & Information

3. Linear Algebra & Optimization

4. Linear Regression & Bayesian Linear Regression

5. Perceptron & Logistic Regression

6. Learning theory, Bias-Variance

7. Model Selection

8. k Nearest Neighbour & Locally Weighted Regression

9. Multilayer Perceptrons

10. Deep Learning

11. Convolutional Neural Networks

12. Recurrent Neural Networks

13. Autoencoders

14. Feature Extraction

15. K-Means, EM-Clustering

16. PCA, ICA

17. Decision Trees

18. Ensemble Methods

19. Kernel Methods & RBF

20. Support Vector Machines

21. Bayesian Networks

22. Stochastic Methods

23. Applications

24. Conclusion