Units:

6

Description:

This course will provide the expertise and skills necessary for applying machine learning techniques to large real-world datasets in order to facilitate knowledge discovery, predictive analytics and decision-support. A variety of sophisticated techniques for refining, visualizing, exploring and modeling data will be introduced and demonstrated. The advantages and disadvantages of linear, nonlinear, nonparametric and ensemble methods will be discussed while exploring the challenges of both supervised and unsupervised learning. The importance of quantifying uncertainty and communicating confidence in model results will be emphasized. Applications will include visualization, clustering, ranking, pattern recognition, anomaly detection, data mining, classification, regression, forecasting and risk analysis. Participants will obtain hands-on experience during project assignments that utilize publicly available datasets and address practical challenges.

Section C2 (Registration on the Pittsburgh Campus)
Section K2 (Registration at the Kigali Location)

Last Modified: 2017-04-07 9:48AM

Semesters offered:

• Spring 2017
• Fall 2016
• Fall 2015