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IS 517 AO - Methods of Data Science

Campus: Urbana-Champaign

Description:

A dramatic increase in computing power has enabled new areas of data science to develop in statistical modeling and analysis. These areas cover predictive and descriptive learning and bridge between ideas and theory in statistics, computer science, and artificial intelligence. We will cover methods including predictive learning: estimating models from data to predict future outcomes. Regression topics include linear regression with recent advances using large numbers of variables, smoothing techniques, additive models, and local regression. Classification topics include linear regression, regularization, logistic regression, discriminant analysis, splines, support vector machines, generalized additive models, naive Bayes, mixture models and nearest neighbor methods as time permits. Lastly we develop neural networks and deep learning techniques, bridging the theory introduced in the earlier parts of the class to purely empirical methods. We situate the course components in the "data sci

Special Instructions:

Graduate student questions may be sent to ischool-advising@illinois.edu

Academic Program Restrictions:

PHD: Informatics - UIUC

Option 1

Number of Required Visit(s): 0

Course Level: Graduate

Credit: 4

Term(s): Spring


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