CSCI 2033 -- New Course

Tue Dec 8 10:17:29 2009

Approvals Received:
Department
on 12-04-09
by Mary Freppert
(freppert@umn.edu)
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
Max-Min 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)
Auto-Enroll
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:
(course-based or
non-course-based)
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 E-mail 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:

  • They explicitly help students understand what liberal education is, how the content and the substance of this course enhance a liberal education, and what this means for them as students and as citizens.
  • They employ teaching and learning strategies that engage students with doing the work of the field, not just reading about it.
  • They include small group experiences (such as discussion sections or labs) and use writing as appropriate to the discipline to help students learn and reflect on their learning.
  • They do not (except in rare and clearly justified cases) have prerequisites beyond the University's entrance requirements.
  • They are offered on a regular schedule.
  • They are taught by regular faculty or under exceptional circumstances by instructors on continuing appointments. Departments proposing instructors other than regular faculty must provide documentation of how such instructors will be trained and supervised to ensure consistency and continuity in courses.

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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:
  • thinking ethically about important challenges facing our society and world;
  • reflecting on the shared sense of responsibility required to build and maintain community;
  • connecting knowledge and practice;
  • fostering a stronger sense of our roles as historical agents.


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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>
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.  Hands-on recitations with Matlab
exercises.  Weekly or bi-weekly 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
2-3     Systems of Linear Equations: Examples.  Elementary Solution Methods.
    + Global Positioning System
4     Theory of Linear Equations: Complexity. Counting.
5-6     Determinants -- Theory.  Proofs.
    + geometry: Volumes.
    = Matlab: functions, graphical outputs.
7-8     Vector Spaces.  Abstract Linear Spaces. Subspaces. Dimensionality
9     Theory of Linear Equations: Existence, Uniqueness.
10-11   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.
14-15   + Singular Value Decomposition. Data Mining.
    + Principal Component Analysis  Image compression.
    + Non-symmetric 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.