Course Overview

This course covers the study of supervised and unsupervised algorithms in the Machine Learning context. Emphasis on formulating, choosing, applying, implementing, and evaluating machine learning models to capture key patterns exhibited in cross-sectional data and longitudinal data. This course also discusses the considerations of model complexity interpretations and implementation in real-world applications using Python and associated packages. An introduction to Deep Learning is provided in this course. Prerequisites: ADS 501 and ADS 502

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