The%20Amazing%20Year:%20M - PowerPoint PPT Presentation

View by Category
About This Presentation
Title:

The%20Amazing%20Year:%20M

Description:

to the Bears in the Super Bowl, which seemed to mirror what Arizona did to ... Chicago. will look at it, too, and they'll be playing it differently next year. ... – PowerPoint PPT presentation

Number of Views:86
Avg rating:3.0/5.0
Slides: 40
Provided by: kru3
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: The%20Amazing%20Year:%20M


1
The Amazing YearMünster (Germany), Covington
(KY), Bethlehem (PA), and Huntingdon
  • Gerald Kruse, Ph.D.
  • Associate Professor of Computer Science and
    Mathematics
  • kruse_at_juniata.edu
  • http//faculty.juniata.edu/kruse

2
2006-07
  • Faculty Exchange at Fachhochschule (FH) Münster,
    during the fall 2006 semester.Note FH-M is
    also known as the Münster University of Applied
    Sciences, or MUAS. Its Engineering and
    Technology campus is located in Burgsteinfurt,
    about 30 miles outside Münster.
  • Taught Algorithmen und Data Strukturen aka
    Algorithms and Data Structures. It is between
    Juniatas CS 2 (CS 240) and Algorithms, CS
    315.
  • Taught Graphical Programming, an one-semester
    course in Computer Graphics.
  • Sabbatical in Huntingdon, PA, during the spring
    2007 semester, exploring modifications to MA 103,
    Quantitative Methods.

3
Some mathematics
  • Let
  • F one semester faculty exchange
  • S one semester sabbatical
  • P full pay for an academic year
  • F S P

4
We hope to encourage Juniata students to study at
FH-Münster by having a faculty exchange first
  • Faculty

Gerald Kruse
Thomas Weik Juniata Students FH-Münster
Students Tim Auman, Mike Link Robin Segglemann,
Frank Volkmer Sascha Hlusiak, Morin
Ostkamp Moritz Prinz, Thomas Beerman
5
Departing Huntingdon on the Train
6
Our House in the Village of Leer
7
Riding Bikes into the Village of Leer
8
First Day of Grundschule
9
Fuchsjagd (or Foxhunt in English)
10
St. Nicholas
11
To read more about our travel exploits
  • http//kruse5.blogspot.com
  • If you read our travel blog, you will have the
    following questions answered
  • 1. Is it a good idea to have non-prescription
    medical supplements shipped from the United
    States to Germany?
  • 2. Can you get a speeding ticket on the
    autobahn?
  • 3. How bad is inadvertently using Juniatas
    Pcard to purchase tickets at Legoland ?
  • 4. What percentage of Germans visit Majorca?

12
Juniata Students For Dinner
13
A Day in the Life
  • Monday, Tuesday, and Wednesday
  • 0637 Bus from Leer to Burgsteinfurt
  • Lecture from 0800 to 0945
  • Lab from 1000 to 1300
  • 1451 Bus from Burgsteinfurt to Leer
  • Thursday and Friday
  • Breakfast for the family
  • 0837 Bus from Leer to Burgsteinfurt
  • Course prep, research, etc.
  • 1351 Bus from Burgsteinfurt to Leer
  • Afternoon Activities with the Family
  • Sporthalle, Horseback Riding, Bike Rides, Fußball

14
The Computer Lab
15
Observations and Reflections
  • Very little homework to grade
  • If the student completes all labs, they are,
    eligible to take the final exam
  • The students were amazing
  • No committee work, a little advising
  • Web-based (foreshadowing to sabbatical)
  • Psst, dont share this with my colleagues at
    Juniata, they might be jealous

16
The Perfect Storm
  • Unscheduled time at the end of the week
  • An Algorithms course filled with eager students,
    who happen to use a variety of computers and
    compilers
  • A troublesome/intriguing timing result
  • A problem with a pleasing blend of Mathematics
    and Computer Science

17
How Fast is my Sorting Algorithm?
  • The Sorting Problem, from Cormen et. al.
    Input A sequence of n numbers, (a1, a2, an)
  • Output A permutation (reordering) (a1, a2,
    an) of the input sequence such that a1 a2
    anNote This definition can be expanded to
    include sorting primitive data such as characters
    or strings, alpha-numeric data, and data records
    with key values.
  • Sorting algorithms are analyzed using many
    different metrics expected run-time, memory
    usage, communication bandwidth, implementation
    complexity, we chose expected run-time
  • Expected running time is given using Big-O
    notation
  • O( g(n) ) f(n) pos. constants c and n0
    s.t. 0 f(n) cg(n) n n0 .
  • While O-notation describes an asymptotic upper
    bound on a function, it is frequently used to
    describe asymptotically tight bounds.

18
Heaps
  • Conceptually, a heap can be thought of as a
    complete binary tree

19
Heaps
  • Conceptually, a heap can be thought of as a
    complete binary tree
  • But in practice, heaps are usually implemented
    as arrays.
  • Notice how 8, 2, and 4 are near each other in the
    tree, but relatively far apart in the array
  • By the way, e-Bay uses a heap-like data
    structure to track bids.

16
14
10
8
7
9
3
2
4
1
A
20
Heapsort
  • Heapsort(A)
  • BuildHeap(A)
  • for (i length(A) downto 2)
  • Swap(A1, Ai)
  • heap_size(A) - 1
  • Heapify(A, 1)

When the heap property is violated at just one
node (which has sub-trees which are valid heaps),
Heapify floats down the parent node to fix the
heap. Remembering the tree structure of the
heap, each Heapify call takes O(lg n) time.
Since there are n 1 calls to Heapify,
Heapsorts expected execution time is O(n lg n),
just like Quicksort.
21
Timing Results
22
Counting Comparisons
23

24
  • Future Work
  • For very large n, we would expect a slowdown for
    ANY algorithm as the data no longer fits in
    memory, but it would look like a step function at
    each layer of memory, not the gradual growth
    Heapsort exhibits.
  • Consider the memory access patterns of Heapsort,
    and attempt to understand, and possibly simplify,
    the mathematic characterization.
  • No one has really fixed the algorithm, either.
  • Explore modifications to the RAM model used in
    theoretical analysis
  • This is a fun exploration for students, appealing
    to those with an interest in the mathematics or
    computer science , look to expand into a student
    research project

25
Quantitative Literacy at Juniata
  • Juniata has had Quant-Math and Quant-Stat
    skill requirement for graduation since the
    mid-1990s.
  • From the Juniata Catalog
  • Quantitative Skills
  • To demonstrate quantitative literacy, students
    have three options
  • (1) complete a "Q" course
  • (2) complete a mathematical course (QM) and a
    statistics course (QS)
  • (3) pass a proficiency exam.

26
Oh, and just what is Quantitative Literacy?
  • The ability to use numbers and data analysis in
    everyday life. Bernard Madison, Univ. of
    Arkansas
  • ..knowing how to reason and think, and it is all
    but absent from our curricula today. Gina
    Kolata, NY Times
  • Having comfort with arithmetic, data analysis,
    computing, modeling, statistics,
    chance/probability, and reasoning. Excerpt from
    Mathematics in Democracy.
  • While a course in quantitative literacy might
    focus on
  • practical, real-world problems, it still provides
    the students
  • with a strong mathematical foundation.

27
MA 103, Quantitative Methods, aka QM
  • MA 103, Quantitative Methods, was developed by
    Sue Esch to serve students who do not have
    courses with quantitative components in their
    POEs.
  • MA 103 is one of the few courses which satisfies
    both the QM and QS skills.
  • A large percentage of students at Juniata satisfy
    their Q graduation requirement by taking MA
    103, Quantitative Methods (5 sections per year).

28
Time for a change
  • From 1996 to 2007, the text used in MA 103 was
    Quantitative Methods, notes written and
    maintained by Sue Esch (Bukowski and Kruse added
    as co-authors later), and produced on campus.
  • Students used two full-feature software packages
    Minitab for statistics, and Maple for
    mathematics.
  • MA 103 was one of my favorite courses to teach,
    but I realized that after 10 years it was due for
    an update.

29
Search Parameters
  • Published texts preferred
  • Excel-based technology preferred
  • Activity-based
  • Many texts considered, three seriously
  • Frequent consultation with Math department
    colleagues

30
And the Winner Is
Chosen Text Quantitative Reasoning, by Alicia
Sevilla and Kay Somers, from Moravian College.
31
Textbook Highlights
  • Active learning approach
  • Technology informs and enhances the math
  • Modules on Apportionment and Conditional
    Probability
  • Fall 2007 Student Comment Regarding the Textbook
  • Despite being outside my major and one of those
    required courses people are supposed to hate, I
    loved this course The textbook for the course
    was one of the best I've had at the school -- it
    was easy to understand, concise, and the
    assignments taught the material well.

32
MAA PREP Workshop at Moravian
  • Shared all my course materials
  • http//www.moravian.edu/QRPREP/photos.html

33
Course Highlights
  • Pre- and post-assessments of student skills and
    attitudes
  • Open-ended projects
  • Paper-reduced (assignments posted online,
    deliverables uploaded)
  • Provided my Math department colleagues with
    daily schedule daily notes suggested
    homework problems solutions to all Activities
  • Web-site evaluation module with Reference
    Librarian
  • Course web-sitehttp//faculty.juniata.edu/kruse/
    ma103/ma103s08syl.htm

34
Fall 2007 Comments
  • I feel that the methods used in this course were
    very effective in teaching the course subject
    matter. Using class time for both lecture and
    working on assignments was great - we apply what
    we learned right away and help was readily
    available when we needed it.
  •  
  • I thought that the instruction and layout for
    this course were excellent. I was very nervous
    about taking a math course, and I had heard bad
    things about QM from past students, but it must
    be the fact that the course was revamped that
    made it so much better.
  •  
  • I really liked political psychology

35
Fall 2007 Comments, cont.
  • This was a very good class for me. I am not very
    confident in my math abilities and this class was
    a nice way for me to gradually get into college
    level math. This class did (not) cause any really
    serious stress
  •  
  • I think that this course was really good. Math
    is not really my favorite subject and I was a
    little disappointed that I would have to take one
    for the FISHN requirements, but Professor Kruse
    did an excellent job. I really feel like I
    learned a lot of valuable things in his class and
    he made math really fun. Instead of it being
    numbers and ideas that I felt like I would never
    use, I feel like I can take and apply everything
    he taught us to real life.

36
Additional Accomplishments
  • At-large member of International Education
    Committee
  • FH-Muenster Kolloquium Googles Billion Dollar
    Eigenvector
  • SIGCSE Special Session A Status Report from the
    Committee to Evaluate Models of Faculty
    Scholarship
  • MAA (Allegheny Mountain Section) Talk Are
    Quicksort and Heapsort Really O(nlg n)?

37
Additional Accomplishments II
  • Published in si.com
  • Jerry of Huntingdon, Pa.,
  • Has presented Andrew with such a lengthy resume
    that the head of our
  • department wanted to hire him. No, Jerry says,
    just answer one question for
  • me. And what might that be, your Lordship? Will
    everyone look at what Indy did
  • to the Bears in the Super Bowl, which seemed to
    mirror what Arizona did to
  • them, which is attacking the Tampa Two defense
    with underneath stuff, and go
  • at them the same way? Oh sure, they all do that.
    It's a copycat league. Chicago
  • will look at it, too, and they'll be playing it
    differently next year. The Cards
  • attacked the Bear nickel and dime, which were
    vulnerable at that stage of the
  • game. Indy? Superior personnel played a great
    role in this as well. Joseph
  • Addai was simply terrific, better than the guys
    trying to tackle him, no matter
  • what defense they were in. Thanks for your
    compliments, Jerry, and may I
  • leave you with these parting words. Never
    underrate the matchup of personnel.
  • It's more important than which attack plugs into
    which defense.
  • http//sportsillustrated.cnn.com/2007/writers/dr_z
    /05/11/mailbag/2.html

38
Thank You!Any Questions?
39
  • Bibliography

T. H. Cormen, C. E. Leiserson, R. L. Rivest, and
C. Stein, Introduction to Algorithms, Second
Edition, Cambridge, MA/London, England The MIT
Press/McGraw-Hill, 2003. N. Dale, C. Weems, D.
T. Joyce, Object-Oriented Data Structures Using
Java, Boston, MA Jones and Bartlett, 2002. M.
T. Goodrich and R. Tamassia, Algorithm Design
Foundation, Analysis, and Internet Examples,
Wiley New York 2001. D. E. Knuth, The Art of
Computer Programming, Volume 3 (Second Edition)
Sorting and Searching, Addison-Wesley-Longman
Redwood City, CA, 1998. C. C. McGeoch,
Analyzing algorithms by simulation Variance
reduction techniques and simulation speedups,
ACM Computing Surveys, vol. 24, no. 2, pp. 195
212, 1992. C. C. McGeoch, D. Precup, and P. R.
Cohen, How to find the Big-Oh of your data set
(and how not to), Advances in Intelligent Data
Analysis, vol. 1280 of Lecture Notes in Computer
Science, pp. 41 52, Springer-Verlag, 1997. R.
Sedgewick, Algorithms in C, Parts 1-4
Fundamentals, Data Structures, Sorting,
Searching, Third Edition, Addison-Wesley
Boston, MA, 1997
About PowerShow.com