TEACHING NEURAL NETWORKS CONCEPTS AND THEIR LEARNING TECHNIQUES Ganesh K. Venayagamoorthy Department of Electrical and Computer Engineering University of Missouri – Rolla, MO 65409, USA gkumar@ieee.orgAbstractNeural networks have become increasing popular in the fields of Science and Engineering overthe last decade. Most graduate schools in the United States of America and probably in otherparts of the world have started offering neural networks as a graduate/postgraduate course.Neural networks are used for nonlinear systems modeling, estimation and prediction ofparameters, pattern matching
-face lectures and online supplemental materials. This hybrid approach to instructionaldelivery has been tailored by each faculty member to fit their disciplinary interests, specificpedagogical aims, assessment practices, and philosophical intentions. Through weekly meetings,the authors have shared experiences in using KSOL as instructional tool and discovered thesystem's valuable role in their scholarship of teaching as well as an instrument for collaborativescholarship.Building a Community of LearnersStage I (1999-2002)In 1999, three KSU Salina’s faculty (Barnard, Leite, Oh) started an attempt to use KSOL,originally designed for distance education, to see if it would facilitate and enhance studentlearning in the traditional face-to-face
students still have rather limited practicalunderstanding of how to apply these basic principles to laboratory measurements thatinvolve real time-varying signals. Courses involving the detailed statistical treatment oftime-dependent random signals are not part of the MNE curriculum since they generallyhave prerequisite requirements beyond the reach of our typical undergraduate students.In addition, while available course textbooks (e.g., [1], [2]) usually provide a gooddiscussion of the statistical treatment of random errors, they do not generally address thepractical issue of how to actually perform independent sampling of time-series data.The Sampling ProblemIt is typically assumed that the samples of measured variables used in statistical
portfolios. Recent searches for faculty in engineering technology and mathematicspositions at Kansas State University yielded few applications which incorporated evidences ofteaching performance beyond statements of classes taught and possibly statements of teachingphilosophy.Lack of historical use and examples of teaching portfolios has been partially responsible for alack of adoption. Others resist consideration of a practice which seems to be yet anotherpaperwork burden. However, the teaching portfolio should not be viewed as a rigid documentrequirement, but rather as a flexible opportunity for summarizing and documenting teachingeither for performance evaluation or for teaching improvement. Material and structure can beselectively adapted and
altered the face of industryand methodology for conducting business. Outsourcing, downsizing, and other factors have ledmany individuals to consider academic options to restructure their careers. This work inprogress will explore these factors and discuss potential implications and research directions. Education is the key to fulfilling goals for advancement in most professional fields. It isbecoming more difficult for an individual without a college degree to rise through the corporateranks or command a high salary. In general, formal education is required for professionalsuccess. In addition, formal education, particularly the completion of a degree, showstrainability. Sometimes a degree is referred to as the union card that gives job
factors to consider are the type of questions toask; for instance: 1) Which of the five evaluation method categories (see above) should be used? 2) How much of the test should be “100% or 0%” versus allowing partial credit? 3) Does working through a calculation demonstrate that the student understands the problem to the cognitive level of understanding to which you want to test?In lieu of constructing an examination from calculations, Selected Response and ConstructedResponse questions can test the students understanding and not allow their calculators to derivethe answer for them. Both the Selected Response and Constructed Response questions have thefollowing advantages: understanding cannot be easily bluffed, the
preference, the library developed a series of online tutorial modules. The modulesprovide faculty and students with library assistance anytime, anywhere and allows them to learnskills at their own pace. K-State Online (http://Online.ksu.edu), an Internet based coursedelivery program, was selected as the means for tutorial delivery. The platform was selectedover traditional web delivery because of its ability to track student usage and monitor theirprogress through the use of quizzes placed at the end of each module. To date, five modulesincorporating fifteen lessons have been developed focusing on general library services,navigating the library’s website, searching the card catalog, library database usage, topicselection, evaluating web sites, and