In systems where parameters can vary, we often want to understand the effects that some variables exert on others and their impact on system performance. “Data Analytics and Predictive Modeling” describes a variety of machine learning and data analysis techniques that can be used to describe the interrelationships among such variables. In this course, we will examine these techniques in detail, including data cleansing processes, data clustering, associate analysis, linear regression analysis, classification methods, naïve Bayes, neural networks, random forests, variable screening methods, and variable transformations. Cases illustrating the use of these techniques in engineering applications will be developed and analyzed throughout the course.

In systems where parameters can vary, we often want to understand the effects that some variables exert on others and their impact on system performance. “Data Analytics and Predictive Modeling” describes a variety of machine learning and data analysis techniques that can be used to describe the interrelationships among such variables. In this course, we will examine these techniques in detail, including data cleansing processes, data clustering, associate analysis, linear regression analysis, classification methods, naïve Bayes, neural networks, random forests, variable screening methods, and variable transformations. Cases illustrating the use of these techniques in engineering applications will be developed and analyzed throughout the course. Note: Students required to take ISEE-561 for credit may not take ISEE-661 for credit.

This course presents the primary concepts of decision analysis. Topics important to the practical assessment of probability and preference information needed to implement decision analysis are considered. Decision models represented by a sequence of interrelated decisions, stochastic processes, and multiple criteria are also addressed. We cover EMV and Non-EMV decision-making concepts. Finally, the organizational use of decision analysis and its application in real-world case studies is presented.

Forecasting Methods will provide the engineering student with the skills necessary to perform data driven time series analysis from an engineering applications perspective. A process driven approach will be used covering the entire forecasting process from data preparation and pre-processing techniques to model selection, performance evaluation, and monitoring. A special emphasis will be placed on performance evaluation and improvement of models used to predict RIT energy demand and peak load days. The course will cover topics in data cleansing, data transformation, trend and seasonality analysis, smoothing techniques, regression analysis for forecasting, seasonal and non-seasonal ARIMA models, dynamic regression, neural networks and advanced modeling techniques for multivariate time series analysis. Lectures and assignments will focus on predicting RIT energy demand considering circuits with 2MW solar fields or similar data sets.