Who should attend?

This course is intended for engineers, data analysts, scientists, managers and others who would like to understand the basic principles underlying statistical learning and data mining. For "tall" data we cover a wide range of techniques for supervised learning from linear regression through various classes of more flexible models to fully nonparametric regression models such as boosting and neural networks, both for the regression problem and for classification. For "wide" data we discuss various forms of regularized model fitting, including many of those used routinely in the analysis of genomic data.

Although a firm theoretical motivation is presented, the emphasis is on practical applications and implementations. The course includes many examples and case studies, and participants should leave the course well-armed to tackle real problems with realistic tools.

No previous exposure to data science is necessary although a degree in engineering or science (or equivalent experience) is desirable. Those attending can expect to gain an understanding of the current state-of-the-art and be in a position to make informed decisions about whether this technology is relevant to specific problems in their area of interest.

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