Statistical Analysis and Machine Learning

This course provides an introduction to machine learning and statistical data analysis. The first part of the course covers topics such as parameter estimation, hypothesis testing and regression analysis. The second part includes machine learning topics such as supervised learning; unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); Neural Networks, Decision Trees, Local Models, Model selection, Combining Multiple Learners, reinforcement learning and adaptive control. During this course, students will learn how to solve real-life problems using state-of-the-art technologies developed for machine learning computing and data analyses.

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