DCS860A Emerging Information Technologies I

Instructors:   Chuck Tappert and Sung-Hyuk Cha

Graduate Assistant: Sukesh Moolya

Textbooks: Data Mining (3rd Ed.), Witten, Frank, and Hall; Morgan Kaufmann 2011.
Big Data, Mayer-Schonberger and Cukier, 2012.
Physics of the Future, Kaku, 2011.
Singularity Rising, Miller, 2012.

Other recommended but not required books

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, such as the readings and quizzes, 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 and analytics, biometrics, pattern recognition, 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 much of the course information. Blackboard is used for quizzes and protected information. Links in the left menu area of the course website are to:

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

Graded Events and Grade Scale

Student evaluations are based on the following graded events and grade scale.

Five quizzes (each with 30 minute time limit) taken via Blackboard.

Team assignment: presentation demonstration of, and associated 5-10 page Word-for-Windows paper on, a creative solution to an interesting data mining problem using Weka, Matlab, or other appropriate software.

Incompletes: in order to be fair to those students who complete the course in a timely manner, the course 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
Quizzes (5 * 20 points) 100 points
Data Mining Team Project 400 points
Totals max 500 points