Pattern-Recognition-and-Machine-Learning2

These are the exercises on my Pattern-Recognition-and-Machine-Learning courses.

The course content includes:

1. Introduction to Python.
2. Estimation Theory.
3. Estimation Theory. Detection Theory.
4. Detection Theory. ROC and AUC.
5. Classification. K-NN classifier. Linear Classifiers. The LDA. Using Scikit-learn.
6. Linear Classifiers. The LDA and the role of projection.
7. Lecture cancelled. Watch last year video from the link.] SVM and the kernel trick. Logistic Regression. Random Forest. Other ensemble methods in sklearn: ExtraTreesClassifier, AdaBoostClassifier and GradientBoostingClassifier.
8. Ensemble classfiers, neural networks.
9. Convolutional networks and deep learning.
10. Convolutional networks.
11. Convolutional networks, Recurrent nets.
12. Recurrent networks. Applications of deep learning.
13. Performance assessment: Cross-validation. Regularization, feature selection.