STATISTICAL LEARNING AND DATA SCIENCE
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.
Who should attend?
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.