(New page: Category:ECE662Spring2012Boutin Category:blog =Lecture 4 Blog, ECE662 Spring 2012, Prof. Boutin= Thursday January 19, 2012 (Week 2) ---- We had a good attend...)
 
 
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=Lecture 4 Blog, [[ECE662]] Spring 2012, [[user:mboutin|Prof. Boutin]]=
 
=Lecture 4 Blog, [[ECE662]] Spring 2012, [[user:mboutin|Prof. Boutin]]=
 
Thursday January 19, 2012 (Week 2)  
 
Thursday January 19, 2012 (Week 2)  
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Quick link to lecture blogs: [[Lecture1ECE662S12|1]]|[[Lecture2ECE662S12|2]]|[[Lecture3ECE662S12|3]]|[[Lecture4ECE662S12|4]]|[[Lecture5ECE662S12|5]]|[[Lecture6ECE662S12|6]]|[[Lecture7ECE662S12|7]]|[[Lecture8ECE662S12|8]]| [[Lecture9ECE662S12|9]]|[[Lecture10ECE662S12|10]]|[[Lecture11ECE662S12|11]]|[[Lecture12ECE662S12|12]]|[[Lecture13ECE662S12|13]]|[[Lecture14ECE662S12|14]]|[[Lecture15ECE662S12|15]]|[[Lecture16ECE662S12|16]]|[[Lecture17ECE662S12|17]]|[[Lecture18ECE662S12|18]]|[[Lecture19ECE662S12|19]]|[[Lecture20ECE662S12|20]]|[[Lecture21ECE662S12|21]]|[[Lecture22ECE662S12|22]]|[[Lecture23ECE662S12|23]]|[[Lecture24ECE662S12|24]]|[[Lecture25ECE662S12|25]]|[[Lecture26ECE662S12|26]]|[[Lecture27ECE662S12|27]]|[[Lecture28ECE662S12|28]]|[[Lecture29ECE662S12|29]]|[[Lecture30ECE662S12|30]]
 
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We had a good attendance today, considering the treacherous road conditions. Good thing it is, because we covered the most basic principle of statistical decision theory today, namely Bayes decision rule. We first presented the rule for discrete-valued feature vectors, and illustrated it using the previously discussed example of determining a person's gender from his/her hair length. We finished the lecture by generalizing to the case of continuous-valued feature vectors.  
 
We had a good attendance today, considering the treacherous road conditions. Good thing it is, because we covered the most basic principle of statistical decision theory today, namely Bayes decision rule. We first presented the rule for discrete-valued feature vectors, and illustrated it using the previously discussed example of determining a person's gender from his/her hair length. We finished the lecture by generalizing to the case of continuous-valued feature vectors.  
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==Relevant Rhea Pages==
 
==Relevant Rhea Pages==
 
*[[Lecture_3_-_Bayes_classification_OldKiwi|A student's notes for Lecture 3 from ECE662 Spring 2008]] (introducing Bayes Rule)
 
*[[Lecture_3_-_Bayes_classification_OldKiwi|A student's notes for Lecture 3 from ECE662 Spring 2008]] (introducing Bayes Rule)
*[[Lecture_4_-_Bayes_Classification_OldKiwi|A student's notes for Lecture 3 from ECE662 Spring 2008]] (discussing Bayes rule for continuous-valued feature vectors)
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*[[Lecture_4_-_Bayes_Classification_OldKiwi|A student's notes for Lecture 4 from ECE662 Spring 2008]] (discussing Bayes rule for continuous-valued feature vectors)
  
  

Latest revision as of 12:30, 23 February 2012


Lecture 4 Blog, ECE662 Spring 2012, Prof. Boutin

Thursday January 19, 2012 (Week 2)


Quick link to lecture blogs: 1|2|3|4|5|6|7|8| 9|10|11|12|13|14|15|16|17|18|19|20|21|22|23|24|25|26|27|28|29|30


We had a good attendance today, considering the treacherous road conditions. Good thing it is, because we covered the most basic principle of statistical decision theory today, namely Bayes decision rule. We first presented the rule for discrete-valued feature vectors, and illustrated it using the previously discussed example of determining a person's gender from his/her hair length. We finished the lecture by generalizing to the case of continuous-valued feature vectors.

Relevant Rhea Pages


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