Tue Dec 8 10:17:29 2009
Approvals Received: 



Approvals Pending:  College/Dean > Catalog  
Effective Status:  Active  
Effective Term:  1109  Fall 2010  
Course:  CSCI 2033  
Institution: Campus: 
UMNTC  Twin Cities UMNTC  Twin Cities 

Career:  UGRD  
College:  TIOT  Institute of Technology  
Department:  11108  Computer Science & Eng  
General  
Course Title Short:  Elem Comput Linear Algebra  
Course Title Long: 
Elementary Computational Linear Algebra 

MaxMin Credits for Course: 
4.0 to 4.0 credit(s)  
Catalog Description: 
Matrices and linear transformations, basic theory. Linear vector spaces. Inner product spaces. Systems of linear equations Eigenvalues and singular values. Algorithms and computational matrix methods using MATLAB or similar. Applications with emphasis on the use of matrix methods to solve a variety of computer science problems.  
Print in Catalog?:  Yes  
CCE Catalog Description: 
<no text provided>  
Grading Basis:  Stdnt Opt  
Topics Course:  No  
Honors Course:  No  
Delivery Mode(s):  Classroom  
Instructor Contact Hours: 
4.0 hours per week  
Years most frequently offered: 
Every academic year  
Term(s) most frequently offered: 
Fall, Spring  
Component 1: 
DIS (no final exam) 

Component 2: 
LEC (with final exam) 

AutoEnroll Course: 
Yes  
Graded Component: 
DIS  
Academic Progress Units: 
Not allowed to bypass limits. 4.0 credit(s) 

Financial Aid Progress Units: 
Not allowed to bypass limits. 4.0 credit(s) 

Repetition of Course: 
Repetition not allowed.  
Course Prerequisites for Catalog: 
Math 1271 or Math 1371 or #  
Course Equivalency: 
No course equivalencies  
Consent Requirement: 
No required consent  
Enforced Prerequisites: (coursebased or noncoursebased) 
No prerequisites  
Editor Comments:  <no text provided>  
Proposal Changes:  <no text provided>  
History Information:  11/09 Proposed Class for new requirements for CSci undergraduate degree.  
Faculty Sponsor Name: 
Dan Boley, Yousef Saad, Chuck Swanson  
Faculty Sponsor Email Address: 
boley@cs.umn.edu, saad@cs.umn.edu  
Student Learning Outcomes  
Student Learning Outcomes: 
* Student in the course:
 Can identify, define, and solve problems
Please explain briefly how this outcome will be addressed in the course. Give brief examples of class work related to the outcome. Students will be able to identify problems in computer science that can be solved using the matrix methods taught in this course. Many examples of critical computer science questions solvable by matrix methods will be presented. Examples include finding shortest paths between nodes of a network, solving geometrical problems in graphics, finding approximate models to fit experimental data, accomplishing tasks such as image compression, data mining, dimensionality reduction. Students will learn how to recognize which problems can be cast as a linear algebra problem amenable to matrix methods. Students will learn basic computational tools to apply the linear algebra concepts to problem solving such as MATLAB, Mathematica, Maple or a similar system. How will you assess the students' learning related to this outcome? Give brief examples of how class work related to the outcome will be evaluated. Students will be presented with examples drawn from computer science which they will have to cast into a form amenable to linear algebra methods. Students will be tested on their abilities to recognize which problems can be cast as a linear algebra problem, and to determine which linear algebra methods can be applied to each case. For example, given a model of a computer network, they might be asked which nodes are reachable in a given number of hops or which nodes are most central to the network. Case studies drawn from real computer science examples would be used. A mix of individual and group projects will be used to evaluate the students' problem solving skills. 

Liberal Education  
Requirement this course fulfills: 
None  
Other requirement this course fulfills: 
None  
Criteria for Core Courses: 
Describe how the course meets the specific bullet points for the proposed core
requirement. Give concrete and detailed examples for the course syllabus, detailed
outline, laboratory material, student projects, or other instructional materials or method.
Core courses must meet the following requirements:
<no text provided> 

Criteria for Theme Courses: 
Describe how the course meets the specific bullet points for the proposed theme
requirement. Give concrete and detailed examples for the course syllabus, detailed outline,
laboratory material, student projects, or other instructional materials or methods. Theme courses have the common goal of cultivating in students a number of habits of mind:
<no text provided> 

Writing Intensive  
Propose this course as Writing Intensive curriculum: 
No  
Question 1: 
What
types of writing (e.g., reading essay, formal lab reports, journaling)
are likely to be assigned? Include the page total for each writing
assignment. Indicate which assignment(s) students will be required to
revise and resubmit after feedback by the instructor or the graduate TA. <no text provided> 

Question 2: 
How does assigning a significant amount of writing serve the purpose
of this course? <no text provided> 

Question 3: 
What types of instruction will students receive on the writing aspect
of the assignments? <no text provided> 

Question 4: 
How will the students' grades depend on their writing performance?
What percentage of the overall grade will be dependent on the quality and level of the students'
writing compared with the course content? <no text provided> 

Question 5: 
If graduate students or peer tutors will be assisting in this course,
what role will they play in regard to teaching writing? <no text provided> 

Question 6: 
How will the assistants be trained and
supervised? <no text provided> 

Question 7: 
Write up a sample assignment handout here for a paper
that students will revise and resubmit after receiving feedback on the initial
draft. <no text provided> 

Readme link.
Course Syllabus requirement section begins below


Course Syllabus  
Course Syllabus: 
For new courses and courses in which changes in content and/or description and/or credits
are proposed, please provide a syllabus that includes the following information: course goals
and description; format;structure of the course (proposed number of instructor contact
hours per week, student workload effort per week, etc.); topics to be covered; scope and
nature of assigned readings (text, authors, frequency, amount per week); required course
assignments; nature of any student projects; and how students will be
evaluated. The University "Syllabi Policy" can be
found here
The University policy on credits is found under Section 4A of "Standards for Semester Conversion" found here. Course syllabus information will be retained in this system until new syllabus information is entered with the next major course modification. This course syllabus information may not correspond to the course as offered in a particular semester. (Please limit text to about 12 pages. Text copied and pasted from other sources will not retain formatting and special characters might not copy properly.) Goal: While this class covers the fundamentals of linear algebra, it also teaches how the theory and methods answer many fundamental questions in Computer Science and Computer Engineering. The basic algorithms will also be used to introduce the core concepts of operation counting and computational complexity. Course Description: Matrices and linear transformations, basic theory. Linear vector spaces. Inner product spaces. Systems of linear equations Eigenvalues and singular values. Algorithms and computational matrix methods using MATLAB or similar. Applications with emphasis on the use of matrix methods to solve a variety of computer science problems. Contact Hours: 3 contact hours of lecture plus a contact hour of recitation Workload: 2 or 3 midterms. Handson recitations with Matlab exercises. Weekly or biweekly homeworks. Students will be expected to read approximately 1 chapter per week, or over two weeks for particularly difficult conceptual material. Text: Elementary Linear Algebra with Applications by B Kolman & D R Hill, 9th edition  Prentice Hall 2008. or: Introduction to Linear Algebra by Gilbert Strang, Cambridge Press 2009 The order of the topics listed below may be changed to match the textbook that is chosen for this class. Schedule: Week Topic 1 Elementary Linear Mappings. Applications in Graphics and Statistics + Graphs and Matrices: Paths and Adjacency Matrix. Pagerank. + Correlations. = Elementary Matlab Programming 23 Systems of Linear Equations: Examples. Elementary Solution Methods. + Global Positioning System 4 Theory of Linear Equations: Complexity. Counting. 56 Determinants  Theory. Proofs. + geometry: Volumes. = Matlab: functions, graphical outputs. 78 Vector Spaces. Abstract Linear Spaces. Subspaces. Dimensionality 9 Theory of Linear Equations: Existence, Uniqueness. 1011 Inner Products. Orthogonality. Least Squares. Norms, Condition Numbers, and Numerical Stability. + Data Fitting. = Matlab: advanced data structures. 12 Abstract linear transformations. + Robotics + graphics: Coordinate Transformations 13 Eigenvalues. diagonalization of symmetric matrices. 1415 + Singular Value Decomposition. Data Mining. + Principal Component Analysis Image compression. + Nonsymmetric Eigenproblems: Markov chains, Pagerank, Recurrences. notes: + Denotes worked example: a use of matrix method in Computer Sci/Eng. = Denotes programming topics presented as part of basic material. 
