Topics covered:

• Introduction to Machine Learning and Pattern Recognition
• Probability theory
• Unsupervised learning techniques
• Gaussian Mixture Models. K-Means algorithm. Kernel Density Estimation
• Artificial neural networks. Backpropagation training algorithm
• Deep Learning
• Extremely Randomized Trees