Really great introduction to machine learning. Kayla did a very good job of showing enough info to get you excited about machine learning without losing you in the complexity.
Really good talk; Kayla was able to use enough real terminology to keep people who are comfortable with math interested and gave the audience a good jumping off point to investigate further.
I imagine once she has more examples and experiences from her project starting this year this talk will be even more useful as a practical example of applying machine learning to a real world problem and how to actually approach the problem.
Overall one of my favourite talks of the conference.
Good talk but I think it could have *really* benefitted from some code samples or demos (or even some math). I know the speaker purposely left that out in order to avoid scaring anybody off, but a deeper understanding of Machine Learning (and the samples / algorithms behind it) is actually what I came hoping to find.
Really excellent talk. Kayla has a way of making difficult topics feel accessible, and that she's like, one of us and reporting her findings to us. In a way, that's what a conference should be - professionals reporting on the results of their striving in our shared profession. Only thing to change would be to add some specific examples of code and/or the math involved. Even if it's in Ruby!
Excellent beginner's overview. The talk was finished very early; while recognizing that every machine learning problem is unique, the speaker could have taken more time presenting examples and insights. She said she is attempting a php implementation next year, so I think this would be a better talk for a future conference.
Really was hoping for something technical here, some sample code, maybe pointing in the direction of some open source tools for doing machine learning in PHP, or even calling R from PHP... but, nothing. Basically it sounds like the speaker wanted to get into this but just didn't get very far, other than providing a history lesson on regression analysis.
I do hope she is successful in building or finding some kind of library or tool for this kind of stuff and presenting it in the future.
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Good introduction to Machine Learning.
Would be awesome to add more real life scenarios / examples.
Really great introduction to machine learning. Kayla did a very good job of showing enough info to get you excited about machine learning without losing you in the complexity.
Really good talk; Kayla was able to use enough real terminology to keep people who are comfortable with math interested and gave the audience a good jumping off point to investigate further.
I imagine once she has more examples and experiences from her project starting this year this talk will be even more useful as a practical example of applying machine learning to a real world problem and how to actually approach the problem.
Overall one of my favourite talks of the conference.
Very informative intro to machine learning. And the "I'm Blue" playing at the end was a nice touch.
Good talk but I think it could have *really* benefitted from some code samples or demos (or even some math). I know the speaker purposely left that out in order to avoid scaring anybody off, but a deeper understanding of Machine Learning (and the samples / algorithms behind it) is actually what I came hoping to find.
It's a very hard topic to talk about in a talk and also in just 50 minutes.
Maybe some code samples will help people to find this talk more useful.
Really excellent talk. Kayla has a way of making difficult topics feel accessible, and that she's like, one of us and reporting her findings to us. In a way, that's what a conference should be - professionals reporting on the results of their striving in our shared profession. Only thing to change would be to add some specific examples of code and/or the math involved. Even if it's in Ruby!
Nice overview of a difficult subject.
Excellent beginner's overview. The talk was finished very early; while recognizing that every machine learning problem is unique, the speaker could have taken more time presenting examples and insights. She said she is attempting a php implementation next year, so I think this would be a better talk for a future conference.
Really was hoping for something technical here, some sample code, maybe pointing in the direction of some open source tools for doing machine learning in PHP, or even calling R from PHP... but, nothing. Basically it sounds like the speaker wanted to get into this but just didn't get very far, other than providing a history lesson on regression analysis.
I do hope she is successful in building or finding some kind of library or tool for this kind of stuff and presenting it in the future.