Difference between revisions of "Lecture 1 - Introduction Old Kiwi" - Rhea

Revision as of 21:15, 6 April 2008

Lecture Notes: This was the first day of class. These notes are from the class lecture.

Links to Course Webpages

Main Course webpage

Old Kiwi webpage

New Kiwi webpage

Login: Use your Purdue Career Account username and password.

Note: You must change your password once a month.

Kiwi Week

Monday at noon until Monday at noon.

Textbook Information

Main article: Textbooks_Old Kiwi

There is not a single book that covers all the things that will be discussed in ECE 662. The class will reference four books during the course of the semester as we cover various topics. All four of them are available through the reserves at the engineering library.

Definition and Examples of Pattern Recognition

Main article: What is Pattern Recognition_Old Kiwi.

Pattern Recognition is the art of assigning classes or categories to data.

Decision Surfaces

Main Article: Decision Surfaces_Old Kiwi

Decision surfaces are the boundaries in the feature space that distinguish classes.

Algebraic Geometry

Main Article: Decision Surfaces_Old Kiwi (This is not a typo)

Varieties

Main Article: Varieties_Old Kiwi

A Variety is a mathematical construct used to define a decision surface.

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-[http://balthier.ecn.purdue.edu/index.php/ECE662#Class_Lecture_Notes Class Lecture Notes]
 Lecture Notes:
 This was the first day of class. These notes are from the class lecture.
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 A Variety is a mathematical construct used to define a decision surface.
+[[Category:Lecture Notes]]
-== Lectures ==
-[http://balthier.ecn.purdue.edu/index.php/Lecture_1_-_Introduction 1] [http://balthier.ecn.purdue.edu/index.php/Lecture_2_-_Decision_Hypersurfaces 2] [http://balthier.ecn.purdue.edu/index.php/Lecture_3_-_Bayes_classification 3]
-[http://balthier.ecn.purdue.edu/index.php/Lecture_4_-_Bayes_Classification 4] [http://balthier.ecn.purdue.edu/index.php/Lecture_5_-_Discriminant_Functions 5] [http://balthier.ecn.purdue.edu/index.php/Lecture_6_-_Discriminant_Functions 6] [http://balthier.ecn.purdue.edu/index.php/Lecture_7_-_MLE_and_BPE 7] [http://balthier.ecn.purdue.edu/index.php/Lecture_8_-_MLE%2C_BPE_and_Linear_Discriminant_Functions 8] [http://balthier.ecn.purdue.edu/index.php/Lecture_9_-_Linear_Discriminant_Functions 9] [http://balthier.ecn.purdue.edu/index.php/Lecture_10_-_Batch_Perceptron_and_Fisher_Linear_Discriminant 10] [http://balthier.ecn.purdue.edu/index.php/Lecture_11_-_Fischer%27s_Linear_Discriminant_again 11] [http://balthier.ecn.purdue.edu/index.php/Lecture_12_-_Support_Vector_Machine_and_Quadratic_Optimization_Problem 12] [http://balthier.ecn.purdue.edu/index.php/Lecture_13_-_Kernel_function_for_SVMs_and_ANNs_introduction 13] [http://balthier.ecn.purdue.edu/index.php/Lecture_14_-_ANNs%2C_Non-parametric_Density_Estimation_%28Parzen_Window%29 14] [http://balthier.ecn.purdue.edu/index.php/Lecture_15_-_Parzen_Window_Method 15] [http://balthier.ecn.purdue.edu/index.php/Lecture_16_-_Parzen_Window_Method_and_K-nearest_Neighbor_Density_Estimate 16] [http://balthier.ecn.purdue.edu/index.php/Lecture_17_-_Nearest_Neighbors_Clarification_Rule_and_Metrics 17] [http://balthier.ecn.purdue.edu/index.php/Lecture_18_-_Nearest_Neighbors_Clarification_Rule_and_Metrics%28Continued%29 18]
-[http://balthier.ecn.purdue.edu/index.php/Lecture_19_-_Nearest_Neighbor_Error_Rates 19]
-[http://balthier.ecn.purdue.edu/index.php/Lecture_20_-_Density_Estimation_using_Series_Expansion_and_Decision_Trees 20]