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Course Catalog

MBA716: Machine Learning: Strategy and Execution

Operations Management (Elective)

The Machine Learning: Strategy and Execution course is structured to offer students a general understanding of the concepts and capabilities of machine learning with a specific focus for the unique challenges with deploying machine learning in a corporate setting. This course is a follow-up to MBA 706: Data Analytics: Tools and Opportunities and as such, a solid understanding of data analytics will be assumed.
In this course, students will have the opportunity to examine, design, and support/defend real world machine learning opportunities in business. The comprehensive group project will focus on taking a company through a readiness exercise to assess the potential for machine learning projects in its environment.

Learning Objectives:
I. The  course  will  discuss  the  theory  and  application  of  machine learning models to automate tasks in a corporate environment. The course introduces many of the challenges inherent to deploying machine learning such as data readiness, workforce concerns, and talent gaps as well as common pitfalls that occur with machine learning projects. This elective  will  consist  of  lectures,  case study analysis, leading guest  speakers  from  industry  and  academia,  and an intensive application of project-based exercises. 
II. As an elective, this course is also about being a leader; the framework is designed to be non-traditional by focusing on learning about personal leadership skills within the context of an organization. It assumes a personal interest in the subject. The purpose in learning about machine learning principals and leadership is to enable each student to become a better leader and an entrepreneurial thinker. Considerable introspection and exploration of the inner territory is expected. The underlying theme is that every person can become a leader and that the ability to lead begins with the process of understanding historical context and self-discovery – of finding your own voice. The course is structured to give you an opportunity to practice your leadership skills and analyze yourself in order to prepare for leadership positions.

Student Specific Learning Objectives: 
The primary objective of this course is to introduce and develop your ability to apply machine learning to problems in business context. This course builds upon the student’s foundation math and statistics. Students are expected to have a strong capability for the solution, analysis and synthesis of a wide variety of practical statistical problems in a logical and effective manner.
This course will advance your development of the following machine learning capabilities:
1. Problem solving and decision making skills
Students will acquire the ability to understand how to apply effective, efficient, and innovative solutions, both independently and cooperatively, to current and future problems using machine learning. As a key outcome of the group project, students will be engaged in a high level of business case -- problem identification, formulation and solution together.
2. Working in teams and networks
Students will apply interpersonal understanding, teamwork, and communication. Students will be required to work collaboratively with peers to achieve a common goal through in-class assignments. To demonstrate workplace practice, students will have the opportunity to assume varying leadership position and exhibit sound project management skills
Students will improve or gain capabilities in:
•    Analyzing and presenting results from complex data sets. This includes being able to critically examine selected machine learning literature research with regard to the use of appropriate methods.
•    Demonstrate a capacity to know when machine learning can be applied to a given problem and what potential issues may arise.
•    Demonstrate the capacity to present evidence based findings and report.

InstructorsSectionTermsMeeting InfoEnrollments
Robert Allen
001 Module I
W  2:00 PM-4:50 PM  McColl  2600
45 / 45 (wtlst 12)