Mixture models are probabilistic models used to represent the presence of subpopulations within an overall population. They allow for statistical inferences about the properties of the subpopulations without requiring sub-population identity information. Mixture models can be thought of as compositional models, where members of the population are sampled at random and the total size reading population has been normalized to 1.

This comprehensive course covers various machine learning principles from supervised, unsupervised to reinforcement learning. Topics also touch on neural networks, support vector machines, bias-variance tradeoffs, and many real-world applications. It requires a background in computer science, probability, multivariable calculus, and linear algebra.