ENMA
6060: Innovation and Technology
Course
Description
Typically, technology is viewed
as the product of innovation. This course examines the role that technology
plays in driving innovation. The primary topics covered in a particular
course offering vary from semester to semester.
For the current
course offering, the primary technology set covered falls under the general
heading of data mining and machine learning. The general goal of this course is to provide
current and future engineering managers with an understanding of this field
that will enable them to lead projects utilizing these technologies, and to
manage teams of experts applying these technologies.
Applications
of data mining and machine learning technologies are already common (though
usually invisible) in items such as appliance, photographic equipment,
etc. At higher levels of complexity and
abstraction, these technologies are being applied to decision-making tasks such
as integrated circuit design, medical image analysis, assessment of credit
worthiness, and stock market forecasting.
Technology-assisted decision-making systems based on these technologies
are becoming ubiquitous, and chances are great that individuals in many
different fields will or are already interacting with products, processes, and
decision-making systems that use these technologies.
In this
course, the following five applications associated with data mining and machine
learning will be covered. For the first
four applications, students will perform experiments on real-world datasets
using free open source Weka software.
1. Classification
2. Clustering
3. Association
4. Regression (neural networks)
5. Optimization (genetic algorithms,
particle swarm optimization)
Through
lectures, homework, case studies, and project team work, student will obtain an
insight into appropriate applications for data mining and machine
learning. It is not the objective of this course to prepare experienced
practitioners capable of applying the technologies covered to complex problems
and processes. Rather, the course is
structured to allow students to assess the potential of these technologies for
impacting engineering management and leadership functions, and stimulate the
application of these technologies in their work to create and enhance competitive
advantage.