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Please leave me comment below if you have any questions, if you notice any errors or if you would like to discuss a topic further. | Please leave me comment below if you have any questions, if you notice any errors or if you would like to discuss a topic further. | ||
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=== Review by AMichaux === | === Review by AMichaux === | ||
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This is a great slecture, and the information given below is nitpicking, in case you want to make your video lectures even *more* awesome. | This is a great slecture, and the information given below is nitpicking, in case you want to make your video lectures even *more* awesome. | ||
| − | In general, the pace of the slides is slightly too quick, but okay. You know the topic better than the person watching! There is one exception: some of the early slides flick past too quickly to read. I suppose an interested student could pause the slides to look at the | + | In general, the pace of the slides is slightly too quick, but okay. You know the topic better than the person watching! There is one exception: some of the early slides flick past too quickly to read. I suppose an interested student could pause the slides to look at the formulas, but there's still no explanation of what is happening in these slides. |
Also, when you introduce the Law of Total Probability you fail to mention it by name. That would have been a useful referent for a student new to the topic. | Also, when you introduce the Law of Total Probability you fail to mention it by name. That would have been a useful referent for a student new to the topic. | ||
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Overall, however, the slecture is excellent, and an interested student could simply rewind and rewatch to pick up on the details. Good job! | Overall, however, the slecture is excellent, and an interested student could simply rewind and rewatch to pick up on the details. Good job! | ||
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| + | ==Write other comments/questions below== | ||
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Back to '''[[Derivation_Bayes_Rule_slecture_ECE662_Spring2014_Kim|Derivation of Bayes' Rule]]''' | Back to '''[[Derivation_Bayes_Rule_slecture_ECE662_Spring2014_Kim|Derivation of Bayes' Rule]]''' | ||
Latest revision as of 16:58, 2 May 2014
Questions and Comments for: Derivation of Bayes' Rule
A slecture by Jieun Kim
Please leave me comment below if you have any questions, if you notice any errors or if you would like to discuss a topic further.
Review by AMichaux
This is a great slecture, and the information given below is nitpicking, in case you want to make your video lectures even *more* awesome.
In general, the pace of the slides is slightly too quick, but okay. You know the topic better than the person watching! There is one exception: some of the early slides flick past too quickly to read. I suppose an interested student could pause the slides to look at the formulas, but there's still no explanation of what is happening in these slides.
Also, when you introduce the Law of Total Probability you fail to mention it by name. That would have been a useful referent for a student new to the topic.
At 4:41 minutes, you introduce the expression X = Zk, but it is not clear that the "k" is a subscript. It would be helpful to explicitly link Zk back to the discussion on Total Probability, and that Zk is a set in a partition of Omega. Remember, you are far more familiar with the format of your slides than someone watching it.
Overall, however, the slecture is excellent, and an interested student could simply rewind and rewatch to pick up on the details. Good job!
Write other comments/questions below
Back to Derivation of Bayes' Rule
