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Marketing Project (IEEE)

Course Number:






Course Description



FALL, 2015


Knowledge progression across disciplines, cultures and people is dependent on languages and the capacity to understand and to process the content of semantic spaces.  Given the escalating volumes of information being generated, documented and stored across numerous fields, an expanding languages technology of Natural Language Processing is a rapidly emerging scientific field.  NLP researchers study fundamental problems in automating textual and linguistic data formats in all disciplines for the purpose of analysis, generation, representation and acquisition of textual ‘big data’ application.

Of particular interest to IEEE is the application of NLP and related deep learning systems across engineering disciplines including robotics, aerospace, electronic and mechanical.  Currently, there are many natural language processing engines being employed in the preparation, integration and utilization of significant, diverse and unstructured textual data.  IEEE has observed this emergence and proliferation of natural language engines and programs, and believes that a standard and universally applied engine is now required and relevant across engineering disciplines.  The impact would be far reaching and a significant contribution to scientific advancement in engineering.

 This project and investigation would require the following three primary research phases:

 --Identification of natural language engines and programs in terms of types, developers, users and specific applications.

 --Evaluation of natural language engines and programs with determination of best in class based on established criteria, including the depth of analytics and the process and ease of ingesting data into the engine.

-- Recommendation for commercially-viable natural language solution that will allow IEEE to provide greatest insight to engineers across engineering disciplines.

The project would be Fall term, 2015 (minis 1 and 2) and carry 12 units of credit.  As shared in the project description, numerous disciplines and backgrounds are acceptable.  The project class size is projected to be 8-10 (full and part time MBA) individuals.  Class meeting time will be determined prior to the first class session.  Questions can be directed to John Mather (



Lecture: 100min/wk and Recitation: 50min/wk