These are the exercises on my Pattern-Recognition-and-Machine-Learning courses.
- Introduction to Python.
- Estimation Theory.
- Estimation Theory. Detection Theory.
- Detection Theory. ROC and AUC.
- Classification. K-NN classifier. Linear Classifiers. The LDA. Using Scikit-learn.
- Linear Classifiers. The LDA and the role of projection.
- 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.
- Ensemble classfiers, neural networks.
- Convolutional networks and deep learning.
- Convolutional networks.
- Convolutional networks, Recurrent nets.
- Recurrent networks. Applications of deep learning.
- Performance assessment: Cross-validation. Regularization, feature selection.