Wayne State University – Intelligent Analytics 2017-12-07T11:39:28+00:00

Project Description

Intelligent Analytics

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Summary

In this course students will learn computational intelligence methods to solve complex analytics problems and develop effective decision support systems. While the course will address generic topics such as dimensionality reduction, feature selection, clustering, function approximation, pattern recognition, process modelling, forecasting, and optimization, the focus will be on developing advanced analytics solutions. Particular attention will be given to big data problems and thinking. Course will be project centric with the end goal of developing significant solutions to complex problems.

Student Insights

# StudentsDegreeField of StudyCollaboration Period (days)International Students (%)
25Master,PhDComputer Science10525

Description

In this course students will learn computational intelligence methods to solve complex analytics problems and develop effective decision support systems. While the course will address generic topics such as dimensionality reduction, feature selection, clustering, function approximation, pattern recognition, process modelling, forecasting, and optimization, the focus will be on developing advanced analytics solutions. Particular attention will be given to big data problems and thinking. Course will be project centric with the end goal of developing significant solutions to complex problems.

Objectives

At the end of the course, the successful student will be able to develop good understanding for:
1) In-depth understanding for the strengths and weaknesses of different classes of neural networks
2) The recent progress made by the scientific and technical community in the broader field of computational intelligence (including support vector machines, decision trees, Bayesian networks, deep structure learning, and other upcoming and promising nontraditional methods).
3) Hands-on experience in the application of computational intelligence methods for developing analytics solutions and decision support systems for significant problems in practice.

Expected solution proposals

Students work in teams on a variety of pattern recognition and machine learning applications/challenges. Examples include:
– Production process monitoring for control
– Product demand forecasting
– Manufacturing process modeling for control
– Estimating life-time value of customers for marketing
– Product warranty claims forecasting based on weather and usage patterns
– Estimating surgery duration times based on the nature of the surgery and staff involved
– Predicting in-patient unit admissions in emergency departments
– Evaluating supplier risks

Methods

Variety of Artificial Neural Networks for machine learning, including deep learning. Students will rely on MATLAB computing environment (in particular, Neural Network and Statistics & Machine Learning ToolBoxes) as well as Hadoop/Spark computing platform (SparkML and SparkR) for large-scale data sets/applications.

Top Competencies

Critical Thinking / Problem-solvingCuriosityJudgement and Decision Making