CSC 340
(DRAFT syllabus revision, 1 January 2016)
CSC 340 will focus on the design, implementation, application, and performance of numerical algorithms that are fundamental to scientific computation. Skills gained from this course will allow students to bring together concepts gained in their science, mathematics and computer science courses and apply them to real problems.
CSC 340 is a course which seeks to integrate Science, Technology, Engineering and Mathematics (STEM) content. It is a part of the upper division course requirements for students taking the ABET (legally, the Accreditation Board for Engineering and Technology, Inc.) accredited Systems Option for a Bachelor of Science (BS) in Computer Science (CS). Students enrolled in CSC 340 are required to have taken calculus and data structures previously. In addition, many have fulfilled general education, degree requirements for the CS degree and have therefore taken a full year of a science, such as Biology, Chemistry or Physics, with a laboratory component. Many students have also fulfilled a general education requirement that they take an additional semester of a science in a discipline other than that used to fulfil the laboratory science requirement.
CSC 340 requires that students build computational tools (Technology) and then apply those tools to find solutions for common practical problems (Science, Engineering and MathematicsSEM). Typical problem areas include machine learning to automate classification (Mathematics/Statistics), optimization (SEM), and signal processing (SEM). Example problems include automatically creating machines that can:
1. Minimize error while classifying objects using only data measurements,
2. Reduce the travel distance of a robot arm whose work involves placing chips in a printed circuit board, and
3. Echolocate to find the distance to a target, such as prey.
In each case, there is an underlying scientific problem that motivates a mathematical model and an engineering implementation of a computational machine.
Consider a problem illustrated in Fig.1. The blue diamonds and red squares are located at points corresponding to two measurements of objects. It is clear that the measurements are clustered around distinct central areas and dispersed differently with respect to the horizontal and vertical axes. For example, notice that the diamonds seem centered near (2,0) and they are spread out further horizontally than vertically.
Notice also that is difficult to describe a simple contour
that would delineate a boundary between the diamonds and squares. Accordingly,
one might seek parameters for a simple boundary that would minimize
misclassifications and remain sensitive to the structure evident. Fortunately,
if the measurements are multivariate normally distributed, there is
mathematical tool that can enable one to form such boundaries. Specifically,
the functions:
grade the degree to which the measurements given in x correspond to those of object known to be in class i, where is the mean of class i, is the a priori probability for class i, and is the covariance matrix for class i. There is a function g_{i} for each class i, and one need only calculate the necessary means, covariance matrix, covariance matrix inverse and covariance matrix determinant, and then compare values of g_{i} in order to classify previously unobserved examples x. The class function g that gives the largest value for x identifies the class into which x should be classified. By creating the computational components to perform these matrix operations and comparisons (Technology), students gain practical experience with mathematical tools (Mathematics), such as matrices, in the context of a prototypical scientific or engineering application (Science and Engineering).
Fig. 1. Data representing a machine learning classification problem.
A second example problem is that of minimizing the distance
traveled by a robot arm as it loads with electrical components, places the
components on a printed circuit board and then returns to reload in preparation
to populate another board. Suppose that Fig.2 illustrates a map of component
positions on a printed circuit board. One may seek to find a minimum length
path that would take the part dispenser to every one of the 14 points on the
map and then return to reload. Various techniques may be applied to find such a
path. A naïve approach might seek to enumerate all possible paths (Technology
and some Engineering) and then simply pick the shortest; however, with 14
positions, there are 13!/2 distinguishable paths, and
in general, with n sites there are n!/2 distinct
paths to consider. For small instances, exhaustive enumeration is reasonable
but for many practical problems, it is not.
Thus, students explore a benchmark problem with a variety of
approaches (Engineering and Technology) or heuristics including randomly
creating candidate paths, simulating (Technology) an annealing process (Science
and Engineering) and mimicking the computation possible through an evolutionary
process such as a genetic algorithm (Science, Engineering and Technology).
Thereupon, students are required to implement alternative algorithms and then
to assess the relative merits of each approach (STEM is blended).
Fig. 2. A map of component positions on a printed circuit board.
A third example studied in CSC 340 arises naturally in the echolocation techniques employed by dolphins or bats, and it has been widely implemented in electronic and computational machinery for sonar (sound ranging) and radar (radio ranging). The basic idea is that a signal is transmitted, such as an acoustic pulse, and a receiver “listens” for an echo of the signal. This scenario is illustrated in Fig. 3, which shows a pulse on the left and a received signal on the right. As one would expect, the returned signal (echo) is a noisy approximation to that which was transmitted and is embedded in ambient noise. Thus, one should seek a “good” match rather than an exact match to occur within the received signal. That match is measured using mathematical correlation and the computation of a measure of correlation may be done in a variety of ways. During the course students implement (Technology) the fast Fourier transform (FFT) and then apply their implementation to find not only correlation (Mathematics), but also to perform convolution, to smooth out noisy signals, and to model periodic data (Science and Engineering).
Fig. 3. A transmitted pulse and a noisy received signal containing an echo of the pulse.
This semester, the course meets Tuesday and Thursday from 11:00 AM12:15 PM in BR 165. A detailed schedule of topics that will be presented weekbyweek may be found by following the “schedule” link above.
Robert J. Schilling and Sandra L. Harris, Applied
Numerical Methods for Engineers Using Matlab and C, Brooks/Cole Publishing
Company,
Notice the SLOs given above. The SLOs make this an algorithmsoriented course. For you to have a context in which to demonstrate clearly and unequivocally that you have mastery of the algorithms, the grading scheme reflects a strong emphasis on implementing computer algorithms and applying them to various problems. Thus, you will be asked to engage three projects for which you may apply your personal implementations of the studied algorithms: one is a machine learning task (creating a classifier), the second involves methods for finding solutions for optimization problems (e.g. the traveling salesman problem), and the third focuses upon digital signal processing (for classification, filtering and sonar or radar ranging). Grading will be based upon performance on the projects, identified hereafter as examinations. The first two projects/examinations will be worth a total of 60% (each worth 30%) and the final project/examination will be worth 40% of the final grade. The projects will demand implementation, validation, demonstration, and application algorithms taught during the course and for these, you are required to employ your own personal implementations of each of the algorithms and methods studied.
Notice that correct, personally programmed implementation is
a central component of this course, critical to validation, demonstration, and
application, and therefore, must be taken very seriously. You may NOT use the
library features of any programming language as a source for the analytical
results you submit. For example, many languages possess libraries for matrix
operations (e.g., NumPy) and you may use such built
in functions to verify your implementations; however, you are required to
implement all specified algorithms yourself and for test purposes in CSC 340, NumPy is specifically prohibited, except to verify
your personal implementations of various algorithms. The point is to show what
you can do, not to showcase what language library authors can do.
Incomplete grades
are given only very rarely and only when the student is
 otherwise
passing the course,
 able to
complete the work of the course entirely on his/her own, and
 prevented
from completing the course by verified unforeseen circumstances beyond the
control of the student.
The instructor MUST be able to certify all three of these factors to the chair before assigning a grade of "I".
Gene A. Tagliarini, PhD
Professor of Computer Science
CIS 2038
9627572
W, 9:0011:30 AM, 1:004:00 PM
Office hours on Monday or Friday are available by appointment. Please email for confirmation.
tagliarinig@uncw.edu
Regular attendance and vigorous participation in class are expected but not required. However, if you desire the "benefit of the doubt" in any matter related to your grade in the class, you will routinely be present, ask relevant questions, and cooperate with the instructor as well as the course objectives. Each student is individually and personally responsible for all material covered during each class meeting.
If you have a disability and need reasonable accommodation in this course, you should inform the instructor of this fact in writing within the first week of class or as soon as possible. If you have not already done so, you must register with the Office of Disability Services in Westside Hall (ext. 3746) and obtain a copy of your Accommodation Letter. You should then meet with your instructor to make mutually agreeable arrangements based on the recommendations of the Accommodation Letter.
Course Student Learning Outcomes and
Course Assessment Plan 
Assessment Instruments 

Course Student Learning Outcomes 
Project 1 
Project 2 
Project 3 Final Exam 

1 
Students
develop knowledge of computer data representation and its relationship to
computational error and error propagation. 
X 

2 
Students
develop knowledge of vector and matrix operations (e.g., addition,
subtraction, transpose, multiplication, inverse). 
X 

3 
Students
learn how to find and use eigenvectors and eigenvalues and students implement programs to find these 
X 

4 
Students
implement and learn to use signal processing algorithms. 
X 

5 
Students implement
and learn to use programs to fit data using both linear and nonlinear
functions. 
X 

6 
Students
develop a knowledge of algorithm and implementation alternatives that enables
them to choose appropriately. 
X 

7 
Students develop skills in writing technical reports that
describe findings that arise from application of software that they develop. 
X 

8 
(iSTEM) Students demonstrate knowledge from two or more
STEM disciplines. 
X 
X 
X 
9 
(iSTEM) Students apply STEM problem solving methodologies,
such as the scientific method, the engineering design process, or modeling,
to realworld problems. 
X 
X 
X 
