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Machine Learning: Concepts and Application
March 22 @ 1:00 pm - 5:00 pm
Modern Languages Building (MLB), Room 2001B
Machine learning can be described as a form of data analysis, often even utilizing well-known and familiar techniques, that has bit of a different focus than traditional analytical practice in many disciplines. The key notion is that flexible, automatic approaches are used to detect patterns within the data, with a primary focus on making predictions on future data. Among other topics, we will look at the trade-offs between model interpretability and prediction accuracy, supervised versus unsupervised learning, and regression versus classification problems.
A familiarity with standard regression analysis as typically presented in applied disciplines is assumed. Regarding programming, demonstrations and exercises with R and Python will be provided, so one should have familiarity with either. This will definitely NOT be an introduction to a programming language, an introduction to statistics, nor an introduction to statistical programming specifically. However, you do not need to be an expert in any of those.