The availability of large data sets and computational resources have encouraged the development of machine learning and data-driven models which pose an interesting alternative to explicit and fully structured models of behaviour. A battery of tools are now available which can automatically learn interesting mappings and simulate complex phenomena. These include techniques such as Hidden Markov Models, Neural Networks, Support Vector Machines, Mixture Models, Decision Trees and Bayesian Network Inference . In general, these tools have fallen into two classes: discriminative and generative models. The first attempts to optimize the learning for a particular task while the second models a phenomenon in its entirety. This difference between the two approaches will be addressed in this thesis in particular detail and the above probabilistic formalisms will be employed in deriving a machine learning system for our purposes.
ChaLearn Automatic Machine Learning Challenge
Automatic Machine Learning (AutoML) google