
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. Secondary: 5187 EE/CS
Building
Meetings:
Tuesdays and Thursdays , 9:45am11am, Professor:
Paul Schrater Email:
schrater@umn.edu
Office: Primary:
211 Elliott Hall, Office
hours: 11am12pm Tues, or by appointment
Teaching Assistants:
Daahey Woo dwoo@cs.umn.edu
Sam Zhou zhou@cs.umn.edu
Office Hours:
Sam: 11am1pm Wed, EE/CS 2209
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 1215 page paper (single space). More difficult projects will get better grades if sucessfully completed. You may work in groups of 24. 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 17: Final report (10 to 15 pages).
Example Projects (from previous classes):
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 Recognition and Machine Learning, Christopher M. Bishop, Springer 2006.
Secondary
(select
chapters from)
Pattern
Classification, 2nd Ed. Duda,
Hart & Stork,Wiley, 2002.
Elements
of Statistical Learning: Data Mining, Inference, Prediction, Trevor Hastie, Robert Tibshirani, and Jerome
Friedman. Springer, 2001.
Gaussian
Processes for Machine Learning, Carl Rasmussen and Christopher K.I.
Williams, MIT Press, 2006.
Week  Tuesday  Thursday  Suggested Readings  Lecture Notes  Assignment 
1 (9/89/10)  Introduction to course  Probability theory and Lin algebra review  Course Syllabus, Matlab Tutorials(1,2),  Lec1 Lec2  
2 (9/159/17) 
Overview of Pattern Recognition  Overview of Pattern Recognition  PRML: Chap 1  Lec3&4  
3 (9/229/24)  No class  No class  
4 (9/2910/1)  CLASSIFICATION Linear Classifiers 
Linear Classifiers  PRML: Chap 4 
Lec5 Lec6  
5 (10/610/8)  Neural Networks  Neural Networks  PRML: Chap 5
DudaHartStork:
Chap 4  Lec7  
6 (10/1310/15)  Patt Rec
example: Handwritten digit classification Matab for Pattern Rec.  Nonlinear Disc Analysis: Support vector machines  DudaHartStork: Chap 5 PRML: Chap 6.16.3  LogisticReg.pdf  Homework 2 bullseye.mat GaussianRegionsDisplayUtility.m Due Date 10/29 
7 (10/2010/22)  SVM  SVM  PRML: Chap 7 Burgestutorial  Lec12PattRec09.pdf kernels.pdf 

8 (10/2710/29)  RVM  PRML: Chap 7  
9 (11/311/5)  LEARNING FUNCTIONAL MAPPINGS Regression and Basis functions  Regression and Basis functions  PRML: Chap 3 Hastie et al. Chap 3, Chap 5  RegressionPartI.pdf RegressionII.pdf  
10 (11/1011/12)  SVM regression  SVM
regression  SV regression tutorial  BayesRegress.pdf RegressionIII.pdf  HW3.zip svm.zip 
11 (11/1711/19)  Bayesian Regression  Gaussian Processes  GP Tutorial Link to GP book  gpnt06.pdf  
12 (11/2411/26)  Gaussian Processes  Graphical Models  PRML: Chap 8  
13 (12/112/3)  Graphical Models  Thanksgiving  PRML: Chap 8  Lec17PattRec09.pdf  HW4.pdf 
14 (12/812/10)  Mixture Models  Dimensionality Reduction  PRML: Chap 9 PRML: Chap 12  prmlslides8.pdf  
15 (12/1512/17)  Sampling Methods  PRML: Chap 11  Sampling.pdf  FINAL PROJECT due Dec. 17th 