Syllabus and Grading Policies

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EE547 will be oriented to hands-on pattern recognition projects, in the form of programming assignments and larger projects using real-world data from a variety of sources such as bioinformatics and computer security. Course prerequisite is basic calculus and introductory statistics.

Website: www.eng.iastate.edu\~julied\classes\ee547
Description: (3-0) Cr. 3. F.  Mathematical formulation of pattern recognition problems and decision functions, statistical approach, Bayes classifier, probability density function estimation, clustering algorithms (supervised and unsupervised), learning algorithms and neural networks, fuzzy recognition systems, feature selection methods.

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Grading:

bulletHomework Assignments and paper/algorithm presentations in class 30%
bulletProject 1 (Report Only) 25%
bulletFinal Project (Report and Presentation) 35%
bulletClass Participation 10%

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Syllabus

Week Topic Chapter
1 Introduction and Overview, examples 1
2
bulletDecision functions and Bayesian decision theory, metrics
bulletNaive Bayes Classifiers, "On the Optimality of the Simple Bayesian Classifier under zero-one Loss", Domingos and Pazzani, Machine Learning, 1997; "An Empirical Study of the naive Bayes classifier," Rish in IJCAI-01 workshop on "Empirical Methods in AI". Also appeared as IBM Technical Report RC22230.
bulletApplication: Spam Detection
 2
3
bulletTopics: Linear Classifiers: Perceptrons, Least squares and mean square error
bulletApplication:
3
4 Nonlinear classifiers 4
5 Nonlinear Classifiers; Kernel-based approaches 4
6 Feature Selection and Processing, statistical methods, project 1 Face Recognition 5.1,5.2,6.1, handouts,
7 Methods of cluster analysis, effects of different metrics 10
8 Clustering Hierarchical 11
9 Clustering Function Optimization 12
10 Cluster validity and assessment 13
11 Feature Selection and Generation 5
12 Tree classification, CART, ID3, C4.5 Papers and website
13 Random Forests, Feature Selection using filters Breiman Website;

Feature Selection links

14

Feature Selection using wrappers, Data visualization

 
15 Classifier Comparison  

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Computer Usage:

The demonstrations presented in this course will be mostly performed in Matlab. However, students are welcome to use any programming or analysis package that they are most comfortable with. All students will be given access to the College of Engineering computer network for their course work and tutorial sessions on how to use Matlab will be held for interested students.

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Page last edited 04/10/2006