CSC 340
(DRAFT syllabus revision, 26 August 2015)
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.
The course meets Monday, Wednesday, and Friday from 1:001:50 PM in CIS 2006. 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 implementations of the studied algorithms: one is a machine learning task (creating a classifier), the second involves finding solutions for optimization problems (), and the third focuses upon digital signal processing (for 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 examinations 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
MW, 9:009:50 AM, 2:00 – 4:30 PM
Other office hours are readily available by appointment.
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 
Test 1 
Test 2 
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 