DCS860A Emerging Information Technologies I

Dr. Charles Tappert

Other Information:
Pace Portal  SurveyMonkey  1 Pace Plaza  

The Second Machine Age, Brynjolfsson and McAfee; Norton 2016.
Machine Platform Crowd, McAfee and Brynjolfsson; Norton 2017.
Other recommended but not required books

Course Description:
     The Emerging Information Technologies two-semester course sequence presents a variety of emerging information technology topics not fully covered in the other DPS course material. Some of the materials covered in the courses are chosen by the instructors with additional topics presented by the student teams and the guest speakers. In covering these materials and in the presentations, many dissertation research possibilities will be discussed.
     The emerging information technology topics covered typically include the technological life cycle, pervasive computing, small computing devices (handheld and wearable computers), communicating with machines in human modalities (voice, handwriting, and natural language applications), wireless communication, big data analytics, biometrics, pattern recognition, machine learning, deep learning, and data mining. The course goals are to learn about the emerging information technologies, their issues and potential impact, and to become aware of various dissertation research possibilities.
     This course provides many opportunities to learn about the emerging information technologies, and particularly those areas requiring further research that could become a dissertation topic. The guest speakers bring you to the frontier of current work in their areas of expertise and present possibilities for further work. The course assignments and team presentations also provide opportunities to investigate topics for potential dissertation work.

Course Website: The course website presents the course information. Blackboard is used for protected information. Links in the left menu area of the course website are to:

Graded Events and Grade Scale

Three team assignments: three big data machine learning algorithms.

Team topic presentation: a team presentation on an emerging IT topic of current interest. Include potential dissertation and discussion topics as appropriate.

Classroom etiquette: please turn cell phones off during class time.

Incompletes: in order to be fair to those students who complete the course in a timely manner, our policy is to reduce the grade of those students taking an incomplete by a letter grade for each semester, or portion thereof, that the incomplete is in effect.

Graded Events
Event Possible points per person
Three Team Assignments 300 points (100 each)
Team Topic Presentation 500 points
Class Attendance/Participation 200 points
Totals max 1000 points

Grade Scale
Grade Definition
A  93-100% Dominates the Material
A-  90-93% Masters the Material
B+  87-90% Good Understanding
with Flashes of Stellar Work
B  83-87% Good Understanding
B-  80-83% Aptitude for the Subject
Less than 80% Weak for Graduate Work