CS 5970 Syllabus

CS 4990/5990/6990 — Indepdendent Studies — Spring 2015

Course Title:

Indepdendent Studies

Instructor:

Prof. Dean F. Hougen, Devon Energy Hall 242, 405-325-3150, hougen@ou.edu

Class Hours:

Monday, 9:30-10:30, Devon Energy Hall 245

Plus either:

Monday, 12:30-1:30, Devon Energy Hall 245 (Speciation)

Monday, 4:30-5:30, Devon Energy Hall 245 (Nurturing)

Office Hours:

Monday 10:30-11:30, Tuesday 10:30-11:30, Thursday 4:30-5:30; Devon Energy Hall 242

Required Text Books:

Each student is required to have his or her own copy of the following textbook:

Writing for Computer Science, Second Edition, Justin Zobel, 2005, Springer. (ISBN 1-85233-802-4)

Each student is also encouraged to have his or her own copy of the following textbook:

Computational Intelligence: An Introduction, Second Edition, Andries P. Engelbrecht, 2007, Wiley. (ISBN: 978-0-470-03561-0)

In addition to the textbooks, there will be readings from the primary, peer-reviewed literature in the field.

Starting Reading Materials:

Brent E. Eskridge and Dean F. Hougen. "Nurturing Promotes the Evolution of Learning in Uncertain Environments." The Second Joint IEEE International Conference on Development and Learning / Epigenetic Robotics Conference, 6 pages, unnumbered, November 2012. http://dx.doi.org/10.1109/DevLrn.2012.6400847

Armand Leonce, Bryan Hoke, and Dean F. Hougen. "Evolution of Robot-to-Robot Nurturing and Nurturability." The Second Joint IEEE International Conference on Development and Learning / Epigenetic Robotics Conference, 7 pages, unnumbered, November 2012. http://dx.doi.org/10. 1109/DevLrn.2012.6400852

Mark Woehrer, Dean F. Hougen, Ingo Schlupp, and Brent E. Eskridge. "Robot-to-Robot Nurturing: A Call to the Research Community." The Second Joint IEEE International Conference on Development and Learning / Epigenetic Robotics Conference, 2 pages, unnumbered, November 2012. http://dx.doi.org/10.1109/DevLrn.2012.6400859

Mark Woehrer, Dean Hougen, and Ingo Schlupp. "Sexual Selection, Resource Distribution, and Population Size in Synthetic Sympatric Speciation." International Conference on the Synthesis and Simulation of Living Systems, pages 137-144 July 2012. http://dx.doi.org/10.7551/978-0-262-31050-5-ch020 Best Paper Award.

Additional reading materials will be added as the semester progresses.

Students should read ahead the chapters and other materials that are expected to be covered in the class period.

Expectations and Goals:

There are no hard prerequisites for this course, other than instructor permission. However, CS 2413 (Data Structures) and Math 3333 (Linear Algebra) will be very helpful as you are expected to have a sufficient background to be able to support team projects involving artificial neural networks and evolutionary computation. You are expected to have a working knowledge of at least one high-level programming language in which you can implement group and individual programming assignments. A background in AI or Machine Learning such as that provided by CS 4013 (Artificial Intelligence) or CS 4033/5033 (Machine Learning) is not a requirement.

The objective of this course is to provide the student with an authentic research experience by integrating the student into an existing interdisciplinary research team that is working in the areas of speciation and robot-to-robot nurturing. The team will develop specific testable hypotheses related to these topics; design, code, and conduct experiments to test these hypotheses; and collect, analyze, and report on the results found.

Computer Accounts and Software:

All students in this class should have an account on the Computer Science Network (CSN). This will be used for writing and testing programs and sending and receiving materials electronically. Source code written for the projects MUST run on these machines. You may do your development work on whatever system you choose but it is your responsibility to ensure that your code runs on the CSN machines.

Grading:

Evaluation of course grade will be based upon participation in research team meetings (including preparation, attendance, and discussion), writing and modifying simulation software, porting software from simulation to real robots, conducting experiments, collecting and organizing data, and assisting with writing up results for publication.

Academic Integrity:

All work must properly cite sources. For example, if you quote a source in one of your technical paper reviews, you must include the quotation in quotation marks and clearly indicate the source of the quotation.

Accommodations:

Any student with a disability should contact the instructor so that reasonable accommodations may be made for that student.

Drop Policy:

Any student who fails to attend the first week of class may be dropped from the class.