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Machine Learning: Concepts and Application

November 16 @ 1:00 pm - 5:00 pm

Modern Languages Building (MLB), Room 2001A

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.

Details

Date:
November 16
Time:
1:00 pm - 5:00 pm
Event Category:

Other

Prerequisite
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.
Class size
24
U-M Affiliated Fee
189
Not U-M Affiliated Fee
420
Register
Instructors
Marcio Mourao & Michael Clark