| Description
| This course covers most of the techniques and algorithms related to the current trend in the machine learning field. The goal of the course is that students complete this course can read and understand the literature in the machine learning field, and can conduct computer simulations for experiments or apply the techniques to practical problems. Therefore, the course intends to cover a broad range of algorithms with emphasis on applications, rather than to cover only a few algorithms with extensive mathematical treatments. The algorithms covered in this course include: foundations of optimizations, random search, decision tree, random decision forests, neural networks and deep learning, genetic algorithm, Bayes' theorem and its applications, Gauss mixture model, hidden Markov model, support vector machine, linear and logistic regressions, clustering algorithms, dimension reduction techniques, boosting methods, decision level fusion techniques, and reinforcement learning.
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