3 ms·

I just showed this link to three classmates who are currently taking the course, and the common reaction was "It's a trap!" They haven't been very satisfied wi

I just showed this link to three classmates who are currently taking the course, and the common reaction was "It's a trap!" They haven't been very satisfied with it. It's co-taught by two professors, one who teaches like it's an introduction for people who have never heard of Bayes' theorem and one who teaches like it's a graduate seminar for people who've seen it all before.

samstave 13y ago
As someone who has never heard of Bayes theorem, is this good or bad? So some things would be explained well and other things over my head?
- deleted 13y ago
  [deleted]
- capnrefsmmat 13y ago
  Right. Some things would be explained from the basics, and some topics would be covered by referring you to obscure papers on advanced techniques in machine learning published by the professors.
psycovic23 13y ago
I took it last semester. Most definitely a trap - Smola is one of those guys who's just too smart to teach. Great material - terrible instruction.
mimighost 13y ago
I am currently in this course. Honestly, this is not a course that I would recommend. The most problematic part of this course is its lack of clear outline. It jumps between different fields of machine learning, which could have fundamentally different focuses and motivations, without illustrating the connections to the students. It talks about Watson-Nadaraya classiﬁer in the second class, then we have two lectures to explain most basic naive bayes algorithm. I just don't get it. Though it gets me confusing a lot of times, the course is useful in a way that gives me a lot of keywords to search for and read article about.Also the homeworks might be challenging some time, working through them did improve my understanding of something I might think trival before, like the linear regression stuff. And if you are really interested, I would recommend a book, which covered most of the materials of the course while being much more organized: http://www-stat.stanford.edu/~tibs/ElemStatLearn/ http://www-stat.stanford.edu/~tibs/ElemStatLearn/ A refresher in linear algebra will also help~
espeed 13y ago
Prof Yaser S. Abu-Mostafa's Caltech course "Learning from Data" (http://work.caltech.edu/telecourse http://work.caltech.edu/telecourse) is probably the best introductory course for really understanding the physics of how machine learning works. See Prof's Yaser's 1 min overview: http://www.youtube.com/watch?v=KlP0DpiM7Lw http://www.youtube.com/watch?v=KlP0DpiM7Lw The "Learning from Data Book" videos are online for free, and the book is on Amazon... Videos: http://home.caltech.edu/lectures.html http://home.caltech.edu/lectures.html Book: http://www.amazon.com/Learning-From-Data-Yaser-Abu-Mostafa/dp/1600490069 http://www.amazon.com/Learning-From-Data-Yaser-Abu-Mostafa/d... The course is also availble on EdX: https://www.edx.org/course/caltechx/cs1156x/learning-data/1120 https://www.edx.org/course/caltechx/cs1156x/learning-data/11...
- eli_gottlieb 13y ago
  I've got the book. It's a great book, even though the Machine Learning course here at Technion is more Bayesian than AML's seemingly PAC and VC-focused book.

samstave 13y ago

As someone who has never heard of Bayes theorem, is this good or bad? So some things would be explained well and other things over my head?

deleted 13y ago
[deleted]
capnrefsmmat 13y ago
Right. Some things would be explained from the basics, and some topics would be covered by referring you to obscure papers on advanced techniques in machine learning published by the professors.

psycovic23 13y ago

I took it last semester. Most definitely a trap - Smola is one of those guys who's just too smart to teach. Great material - terrible instruction.

mimighost 13y ago

I am currently in this course. Honestly, this is not a course that I would recommend. The most problematic part of this course is its lack of clear outline. It jumps between different fields of machine learning, which could have fundamentally different focuses and motivations, without illustrating the connections to the students. It talks about Watson-Nadaraya classiﬁer in the second class, then we have two lectures to explain most basic naive bayes algorithm. I just don't get it. Though it gets me confusing a lot of times, the course is useful in a way that gives me a lot of keywords to search for and read article about.Also the homeworks might be challenging some time, working through them did improve my understanding of something I might think trival before, like the linear regression stuff. And if you are really interested, I would recommend a book, which covered most of the materials of the course while being much more organized: http://www-stat.stanford.edu/~tibs/ElemStatLearn/ http://www-stat.stanford.edu/~tibs/ElemStatLearn/ A refresher in linear algebra will also help~

espeed 13y ago

Prof Yaser S. Abu-Mostafa's Caltech course "Learning from Data" (http://work.caltech.edu/telecourse http://work.caltech.edu/telecourse) is probably the best introductory course for really understanding the physics of how machine learning works. See Prof's Yaser's 1 min overview: http://www.youtube.com/watch?v=KlP0DpiM7Lw http://www.youtube.com/watch?v=KlP0DpiM7Lw The "Learning from Data Book" videos are online for free, and the book is on Amazon... Videos: http://home.caltech.edu/lectures.html http://home.caltech.edu/lectures.html Book: http://www.amazon.com/Learning-From-Data-Yaser-Abu-Mostafa/dp/1600490069 http://www.amazon.com/Learning-From-Data-Yaser-Abu-Mostafa/d... The course is also availble on EdX: https://www.edx.org/course/caltechx/cs1156x/learning-data/1120 https://www.edx.org/course/caltechx/cs1156x/learning-data/11...

eli_gottlieb 13y ago
I've got the book. It's a great book, even though the Machine Learning course here at Technion is more Bayesian than AML's seemingly PAC and VC-focused book.