(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...) |
|||
| (One intermediate revision by the same user not shown) | |||
| Line 4: | Line 4: | ||
=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) | ||
| + | ---- | ||
| + | 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]] | ||
---- | ---- | ||
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. | ||
| Line 9: | Line 11: | ||
==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 | + | *[[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 11:30, 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
- A student's notes for Lecture 3 from ECE662 Spring 2008 (introducing Bayes Rule)
- A student's notes for Lecture 4 from ECE662 Spring 2008 (discussing Bayes rule for continuous-valued feature vectors)
Previous: Lecture 3
Next: Lecture 5
Comments
Please write your comments and questions below.
- Write a comment here
- Write another comment here.
