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This course is intended for beginning
graduate students and advanced undergraduates. We assume students have
a rudimentary understanding of linear algebra, calculus, and are able
to program in some type of structured language. There will be four
homework assignments and a final project. Grading will be
approximately 60% on the homework assignments (15% each) and
40% on the final project. Electrical Eng/Comp Sci 3-111 Secondary: 5-187 EE/CS Building
Meetings:
Mondays and Wednesdays , 11:15-12:30pm,
Professor:
Paul Schrater
E-mail:
schrater@umn.edu
Office:
Primary: 211 Elliott Hall,
Office hours:
12:30pm-2pm Mon, or by appointment
Teaching Assistants:
Kelly Cannon, cannon@cs.umn.edu
Ryan M McCabe, mcca0502@umn.edu
Office Hours:
(Kelly) 12:30-1:30pm Wed in
EE/CSci 2-209 or by appointment under special circumstances
(Ryan) 2:30-3:30pm Tues in EE/CSci 2-209
Final Project Assignment: Your
final project will involve one of the following
1) Simulation or experiments. For example, implement a pattern recognition system for a particular application, e.g. digit classification, document clustering, etc.
2) Literature survey (with critical evaluation) on a given topic.
3) Theoretical work (detailed derivations, extensions of existing work, etc)
In all cases, the work should be written up as a 12-15 page paper (single space). More difficult projects will get better grades if sucessfully completed. You may work in groups of 2 or 3. However, the content must be sufficient for the size of the group. You will be evaluated in terms of the care with which you set up and thought through the goals and implementation, and in terms of the competence of the execution. Regardless of form the write up should include a survey of related literature results.
The project schedule is:
Sept. 28: Topic selection. One or two pages explaining the project with
a list of references.
Nov. 7: Partial report (3 to 5 pages).
Dec 19: Final report (10 to 15 pages).
Graduate students should give a short presentation of their project
towards the last weeks of the semester.
This presentation will count for 5% of the total class grade (this
grade will be counted as part of the project
grade).
Cheating and Plagiarism
The homework and programming assignments
must not be the result of cooperative work. Each student
must work individually in order to understand the material in depth.
You may discuss the issues but by
no means, copy the homework or the programming assignment of somebody
else. All work in the projects
and the programming assignment must properly cite sources. For example,
if you quote a source in your
project, you must include the quotation in quotation marks and clearly
indicate the source of the quotation.
Any student caught cheating will receive an F as a class grade
and the University policies for cheating
and plagiarism will be followed.
Primary:
Pattern Classification, 2nd Ed. Duda, Hart & Stork,Wiley, 2002.
Secondary (select
chapters from)
Statistical Pattern Recognition, 2nd Ed. Andrew Webb,Wiley, 2002.
Neural Networks for Pattern Recognition, Christopher Bishop, Oxford University Press, 1995.
| Week | Tuesday | Thursday | Suggested Readings | Lecture Notes | Assignment |
| 1 (9/7) | Introduction | Probability and Linear Alg. Review | Course Syllabus, Matlab Tutorials(1,2), | Lec1 | |
|
2 (9/12-9/14) |
Probability
and Linear Alg. Review Elementary Decision Theory |
Elementary Decision Theory |
DudaHartStork: Chap 1 |
Lec2, Lec3&4 | Homework 1 Due Date 9/26/03 11:15am |
| 3 (9/19-9/21) | Elementary
Decision Theory Classification/Inference with Parametric models |
Classification/Inference with Parametric models (Example) | DudaHartStork: Chap 2 Webb: Chap 2.1,2.2.1 |
Lec5 Lec6 | |
| 4 (9/26-9/28) | Classification/Inference with Parametric models |
Bayesian Classification/Inference with Parametric models Non-Parametric models: k Nearest Neighbor and kernel density estima |
DudaHartStork: Chap 3 Webb: Chap 2 Webb: Chap 3 |
Lec6&7 Lec8 | |
| 5 (10/3-10/5) | Non-Parametric models:continued | Bayes Nets |
Webb: Chap 3.2.5 DudaHartStork: Chap 4 |
|
|
| 6 (10/10-10/12) | Matab for Pattern Rec. | Linear Discriminant analysis |
Webb: Chap 2.3; Bishop 2.6 Webb: Chap 4.1-4.3.3 |
Examples.m
|
Homework 2 Due 11/7 |
| 7 (10/17-10/19) | Linear Discriminant analysis |
Patt Rec example: Handwritten digit classification |
DudaHartStork: Chap 5
|
|
|
| 8 (10/24-10/26) |
Webb: Chap 6 Webb: Chap 5.1-5.3 |
|
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| 9 (10/31-11/2) | Non-linear Disc Analysis: Support vector machines | SVM |
Webb: Chap 5.4 DudaHartStork: Chap 6 |
||
| 10 (11/7-11/9) | SVM |
SVM |
|||
| 11 (11/14-11/16) | SVM |
Multi-layer NN | Lec13 |
HW3 is posted, due 12/10 by midnight | |
| 12 (11/21-11/23) | Multi-layer NN |
Radial Basis function networks |
DudaHartStork: Chap 6 |
Lec14 |
|
| 13 (11/28-11/30) | Assessing/Improving
Performance |
Feature Extraction/Selection | DudaHartStork:
Chap 10.13-10.14 Webb: Chap 10 DudaHartStork: Chap 9.3-9.6 Webb:Chap 8 Webb: Chap 9 DudaHartStork: Chap 9.3-9.6 |
Lec15 |
|
| 14 (12/5-12/7) | Boosting |
Dimensionality Reduction |
Boosting OriginalPaper Chapter for Lecture DudaHartStork: Chap 10 Webb: Chap 11 |
Lec16 Lec17 |
EX Credit |
| 15 (12/12-12/14) |
FINAL PROJECT due Dec. 19th |