CS 5970 Syllabus

Artificial Neural Networks and Evolution — Spring 2013

Course Title:
Artificial Neural Networks and Evolution (ANNE)

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

Class Hours:

Office Hours:

Required Text Books:
Each student is required 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)
Each student is also encouraged 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)

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

Students should read ahead the chapters and other materials that are expected to be covered in the class period. Students should always bring their textbooks with them to class, including lectures/discussions, group work days, and exams.

Expectations and Goals:
The prerequisites for this course are CS 2413 (Data Structures) and Math 3333 (Linear Algebra) or instructor permission. You are expected to have a sufficient background in Computer Science to be able to support team projects involving artificial neural networks and evolutionary computation. You are expected to have a working knowledge of Java (or another high-level, object-oriented language and a willingness to learn Java). Your programming projects will require the use of Java. 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.

This course will introduce students to the state of the art in artificial neural networks and artificial evolution and cover the principles involved.


The graded assignments and their contribution to a student's grade are given in the table below. (Subject to change.)

Item Quantity Portion of Grade
Exam 1 30%
Homeworks 6 5% (1% each, drop lowest)
Small Projects 2 10% (5% each)
Technical Paper Assignments 1 10%
Class Participation 5%
Large Project 1 40%

During roughly the first half of the semester, we will cover most of the first fourteen chapters of the textbook, as well as outside readings (on grammatical evolution, artificial life, neuro-evolution, etc.). You will have homeworks and small projects based on this material and this portion of the course will conclude with an examination. During this portion of the semester, you will also begin to individually explore papers from the primary, peer-reviewed literature on advanced topics in ANNE, and you will begin to develop ideas and find background material for your large project (see below).

During roughly the second half of the semester, you will present papers on advanced topics in ANNE and complete large projects based on these topics. The large projects will consist of several components, including a topic paragraph, references, a proposal, status reports, code, data, analysis, a report, presentation materials, and presentations. This portion of the semester will conclude with presentations on your large projects.

All homework, exams, small projects, and technical paper reviews and presentations in this course are to be done ALONE; the work submitted by a student MUST be the student's own.

Group work is not required for the large projects but it is allowed. Students will select their own groups and each group will give specific roles and tasks to its group members.

You are responsible for the material covered during the lecture sessions, whether or not it is also found in your textbooks or other assigned reading materials. Similarly, you are responsible for the material found in your textbooks and other assigned reading materials, whether or not it is also covered during the lecture sessions. In other words, you are responsible for the UNION of these sources of knowledge, as depicted by the shaded region of the Venn diagram below, not merely their intersection.

UNION of lectures and readings

You may write your programs from scratch or may start from programs for which the source code is freely available on the web or through other sources (such as friends or student organizations). If you do not start from scratch, you Individual or must give a complete and accurate accounting of where all of your code came from and indicate which parts are original, which are changed, and which you got from which other source. Failure to give credit where credit is due is academic fraud and will be dealt with accordingly.

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.

Late assignments will be penalized 20% per day late. (All parts of days will be rounded up.) After five days, you will not be able to turn in that assignment for credit. If you are worried about turning in the assignment late and loosing points, turn in the assignment ahead of time. You will be turning in electronic and paper copies of group projects. It is the electronic copy that must be turned in by class time on the day that it is due. The paper copy is due twenty four hours after the electronic copy. The paper copy may be submitted in class or turned in during office hours or by slipping it under my office door.

All exams will be open book/open notes. NO electronic devices will be permitted in the testing area.

Copying another's work, or possession of electronic computing or communication devices in the testing area, is cheating and grounds for penalties in accordance with school policies.

Please see the University's web pages on academic integrity.

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.

Student Evaluations:
"The College of Engineering utilizes student ratings as one of the bases for evaluating the teaching effectiveness of each of its faculty members. The results of these forms are important data used in the process of awarding tenure, making promotions, and giving salary increases. In addition, the faculty uses these forms to improve their own teaching effectiveness. The original request for the use of these forms came from students, and it is students who eventually benefit most from their use. Please take this task seriously and respond as honestly and precisely as possible, both to the machine-scored items and to the open-ended questions."

Related Documents:
Students should also read the related documents on Replacement Assignments or Extensions and Discussions of Scores and Grades.