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Don't mean to hijack the comment thread, but can anyone recommend any good videos that introduce machine learning or courses? I studied computer science, but w

Don't mean to hijack the comment thread, but can anyone recommend any good videos that introduce machine learning or courses? I studied computer science, but was not able to take any classes on the subject. I found the Stanford one, anyone have experience with it? http://online.stanford.edu/course/machine-learning http://online.stanford.edu/course/machine-learning

Derander 12y ago
I've heard nothing but good reviews of the online version of the class. I took the class at Stanford (and actually worked on the system mentioned in the article) and I found its content to be useful. I believe that the online version contains less theory but this is not necessarily a bad thing if all you want is an introduction. I've also found reading papers to be illuminating: often the first article about a given classifier is fairly well written and accessible if you have a strong background in math. This is also a useful thing to keep in mind: http://scikit-learn.org/stable/tutorial/machine_learning_map/ http://scikit-learn.org/stable/tutorial/machine_learning_map...
- hoprocker 12y ago
  This looks clipped in my browser, but you can see the original diagram here: http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html http://peekaboo-vision.blogspot.com/2013/01/machine-learning...
pavanred 12y ago
Here are the ones that I found most useful, by Andrew Ng [1] and Daphne Koller [2] on Coursera. [1] https://class.coursera.org/ml-003/lecture https://class.coursera.org/ml-003/lecture [2] https://class.coursera.org/pgm-003/lecture https://class.coursera.org/pgm-003/lecture
skeletonjelly 12y ago
Been meaning to watch these sometime: http://homepages.inf.ed.ac.uk/vlavrenk/iaml.html http://homepages.inf.ed.ac.uk/vlavrenk/iaml.html
dcre 12y ago
I really liked the Caltech course. I didn't spend too much time trying the Stanford one, but I got the sense that this one had more of an overarching theoretical framework that every new concept was explained in terms of. http://work.caltech.edu/telecourse.html http://work.caltech.edu/telecourse.html

Derander 12y ago

I've heard nothing but good reviews of the online version of the class. I took the class at Stanford (and actually worked on the system mentioned in the article) and I found its content to be useful. I believe that the online version contains less theory but this is not necessarily a bad thing if all you want is an introduction. I've also found reading papers to be illuminating: often the first article about a given classifier is fairly well written and accessible if you have a strong background in math. This is also a useful thing to keep in mind: http://scikit-learn.org/stable/tutorial/machine_learning_map/ http://scikit-learn.org/stable/tutorial/machine_learning_map...

hoprocker 12y ago
This looks clipped in my browser, but you can see the original diagram here: http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html http://peekaboo-vision.blogspot.com/2013/01/machine-learning...

pavanred 12y ago

Here are the ones that I found most useful, by Andrew Ng [1] and Daphne Koller [2] on Coursera. [1] https://class.coursera.org/ml-003/lecture https://class.coursera.org/ml-003/lecture [2] https://class.coursera.org/pgm-003/lecture https://class.coursera.org/pgm-003/lecture

skeletonjelly 12y ago

Been meaning to watch these sometime: http://homepages.inf.ed.ac.uk/vlavrenk/iaml.html http://homepages.inf.ed.ac.uk/vlavrenk/iaml.html

dcre 12y ago

I really liked the Caltech course. I didn't spend too much time trying the Stanford one, but I got the sense that this one had more of an overarching theoretical framework that every new concept was explained in terms of. http://work.caltech.edu/telecourse.html http://work.caltech.edu/telecourse.html