6 ms·

Online graduate-level machine learning course from CMU's Tom Mitchell

273 points by ilcavero 15y ago

amirmc 15y ago
"To view a video you will have to login with your CMU Andrew username and password, ..." Also, requires Silverlight (which I don't fancy installing) Edit: This is the Tom Mitchell that Andrew Ng refers to early on in the Stanford ML lectures (when defining Machine Learning)
- ya3r 15y ago
  You don't have to login to watch videos. He is the author of one of the must used texts on machine learning: "Machine Learning, Tom Mitchell, McGraw Hill, 1997."
ya3r 15y ago
As Tom Mitchell says on the first video, this course is recommended for Phd students.
drats 15y ago
Silverlight? Are these people serious? Whether you are an educational institution or a for-profit media company, you are trying to get to the largest number of people and cause them the fewest problems. Silverlight fails spectacularly at both those objectives. Edit: I know there seems to be a flash player component as well, but it's failing for me and can't get to the .mp4. Which doesn't speak well of the joker who cobbled the site together either.
- SkyMarshal 15y ago
  Especially when the target audience for such a class is probably likely to have an outsized portion of *nix users.
- zeratul 15y ago
  Stanford also uses Silverlight and Flash: http://171.64.93.201/ClassX/system/users/web/pg/view_subject.php?subject=CS229_FALL_2011_2012 http://171.64.93.201/ClassX/system/users/web/pg/view_subject... Maybe now it's considered as a "distant learning standard"?
monk_the_dog 15y ago
I'm enrolled in the online Applied ML class from Stanford, and I've also been watching this course from CMU (I'm up to the Graphical Model 4 lecture - almost the midterm). If you've taken at least one stats class you'll get much more out of CMU's class. BTW, here are some good online resources for machine learning: * The Elements of Statistical Learning (free pdf book): http://www-stat.stanford.edu/~tibs/ElemStatLearn/ http://www-stat.stanford.edu/~tibs/ElemStatLearn/ * Information Theory, Inference, and Learning Algorithms (free pdf book): http://www.inference.phy.cam.ac.uk/mackay/itila/ http://www.inference.phy.cam.ac.uk/mackay/itila/ * Videos from Autumn School 2006: Machine Learning over Text and Images: http://videolectures.net/mlas06_pittsburgh/ http://videolectures.net/mlas06_pittsburgh/ * Bonus link. An Empirical Comparison of Supervised Learning Algorithms (pdf paper): http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml06.pdf http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icm... (Note the top 3 are tree ensembles, then SVM, ANN, KNN. Yes, I know there is no 'best' classifier.)
- danso 15y ago
  I love it when people link to freely available academic texts, thank you. Here's another one from Stanford: Mining of Massive Datasets http://infolab.stanford.edu/~ullman/mmds.html http://infolab.stanford.edu/~ullman/mmds.html
  - monk_the_dog 15y ago
    I just took a quick look on the chapter on clustering. Looks good! I'll put it on the ever growing stack. Thanks!
- bhickey 15y ago
  To your list, I'd like to add Jaynes's 'Probability Theory' A few chapters are freely available here: www-stat.wharton.upenn.edu/~steele/Publications/PDF/PT.pdf (The publisher asked the book's editor to stop distributing the whole PDF.)
- zeratul 15y ago
  About the bonus link: It does not make sense to compare ensamble methods (bagging & boosting) with single instance classifiers. In practice, you try all classifiers and then you use best to create an ensamble. The paper leaves me unsatisfied, thinking that probably bagging or boosting SVM would give the best results.
zeratul 15y ago
Three most important issues in ML are missing for this course: * Feature selection, Overfitting, Bias-Variance tradeoff Maybe one of the prof Mitchell's students can make the missing slides available online?
- law 15y ago
  If I'm not mistaken, that was just a recitation that replaced the regular Thursday class. It was one of the TAs covering that stuff briefly. All three topics were covered by Tom Mitchell in previous classes.
Maven911 15y ago
I hope this question doesnt come off as too new naive but due to the amount of links on the front page about ML - what is so fascinating about ML?? Why is there not the same level of interest/links on topics such as cryptology, graphics, circuits, comp architecture ?
- deleted 15y ago
  [deleted]
- kaybe 15y ago
  I find the potential fascinating. ML allows computers to solve problems without a human and become more powerful tools that can eventually act on their own. The extent to which this is possible is not clear yet, but is potentially very far-reaching and can be very empowering for human-kind, in all areas of our existence. The other topics you mentioned are interesting as well, of course, but their potential impact is much lower (although computer architecture can border on ML). (edit typo)
- deleted 15y ago
  [deleted]
- law 15y ago
  There's this enormous focus on 'web scale' technologies. This focus necessarily invokes visualizing and making sense of terabytes and eventually even petabytes of data; conventional approaches would take thousands or millions of man hours to accomplish the same level of analysis that computers can perform in hours or days. Tom Mitchell's definition of machine learning algorithms as those that improve their performance at some task with experience is precisely the way in which humans go about learning what's necessary to perform the same tasks that formerly took thousands or millions of hours. For highly dimensional problems, such as text classification (i.e., spam detection) or image classification (i.e., facial detection), it's almost impossible to hard code an algorithm to accomplish its goal without using machine learning. It's much easier to use a binary spam/not spam or face/not face labeling system that, given the attributes of the example, can learn which attributes beget that specific label. In other words, it's much easier for a learning system to determine what variables are important in the ultimate classification than trying to model the "true" function that gives rise to the labeling.
igrekel 15y ago
Cool. I'm disappointed that there isn't a video for hidden markov models and other models for time series tough, just slides. The schedule says that session is in march, maybe by then there will be a video online.
schiptsov 15y ago
Silverlight is required to use the Panopto viewer. - FUCK YOU!
kky 15y ago
I love that open source mentality (sharing and collaborating for the love of the work, community, and result) is reaching higher ed. I can't wait for it to reach lower ed! If kids start seeing this model at a young age...

273 points by ilcavero 15y ago

"To view a video you will have to login with your CMU Andrew username and password, ..." Also, requires Silverlight (which I don't fancy installing) Edit: This is the Tom Mitchell that Andrew Ng refers to early on in the Stanford ML lectures (when defining Machine Learning)

ya3r 15y ago
You don't have to login to watch videos. He is the author of one of the must used texts on machine learning: "Machine Learning, Tom Mitchell, McGraw Hill, 1997."

ya3r 15y ago

As Tom Mitchell says on the first video, this course is recommended for Phd students.

drats 15y ago

Silverlight? Are these people serious? Whether you are an educational institution or a for-profit media company, you are trying to get to the largest number of people and cause them the fewest problems. Silverlight fails spectacularly at both those objectives. Edit: I know there seems to be a flash player component as well, but it's failing for me and can't get to the .mp4. Which doesn't speak well of the joker who cobbled the site together either.

SkyMarshal 15y ago
Especially when the target audience for such a class is probably likely to have an outsized portion of *nix users.
zeratul 15y ago
Stanford also uses Silverlight and Flash: http://171.64.93.201/ClassX/system/users/web/pg/view_subject.php?subject=CS229_FALL_2011_2012 http://171.64.93.201/ClassX/system/users/web/pg/view_subject... Maybe now it's considered as a "distant learning standard"?

monk_the_dog 15y ago

I'm enrolled in the online Applied ML class from Stanford, and I've also been watching this course from CMU (I'm up to the Graphical Model 4 lecture - almost the midterm). If you've taken at least one stats class you'll get much more out of CMU's class. BTW, here are some good online resources for machine learning: * The Elements of Statistical Learning (free pdf book): http://www-stat.stanford.edu/~tibs/ElemStatLearn/ http://www-stat.stanford.edu/~tibs/ElemStatLearn/ * Information Theory, Inference, and Learning Algorithms (free pdf book): http://www.inference.phy.cam.ac.uk/mackay/itila/ http://www.inference.phy.cam.ac.uk/mackay/itila/ * Videos from Autumn School 2006: Machine Learning over Text and Images: http://videolectures.net/mlas06_pittsburgh/ http://videolectures.net/mlas06_pittsburgh/ * Bonus link. An Empirical Comparison of Supervised Learning Algorithms (pdf paper): http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml06.pdf http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icm... (Note the top 3 are tree ensembles, then SVM, ANN, KNN. Yes, I know there is no 'best' classifier.)

danso 15y ago
I love it when people link to freely available academic texts, thank you. Here's another one from Stanford: Mining of Massive Datasets http://infolab.stanford.edu/~ullman/mmds.html http://infolab.stanford.edu/~ullman/mmds.html
- monk_the_dog 15y ago
  I just took a quick look on the chapter on clustering. Looks good! I'll put it on the ever growing stack. Thanks!
bhickey 15y ago
To your list, I'd like to add Jaynes's 'Probability Theory' A few chapters are freely available here: www-stat.wharton.upenn.edu/~steele/Publications/PDF/PT.pdf (The publisher asked the book's editor to stop distributing the whole PDF.)
zeratul 15y ago
About the bonus link: It does not make sense to compare ensamble methods (bagging & boosting) with single instance classifiers. In practice, you try all classifiers and then you use best to create an ensamble. The paper leaves me unsatisfied, thinking that probably bagging or boosting SVM would give the best results.

zeratul 15y ago

Three most important issues in ML are missing for this course: * Feature selection, Overfitting, Bias-Variance tradeoff Maybe one of the prof Mitchell's students can make the missing slides available online?

law 15y ago
If I'm not mistaken, that was just a recitation that replaced the regular Thursday class. It was one of the TAs covering that stuff briefly. All three topics were covered by Tom Mitchell in previous classes.

Maven911 15y ago

I hope this question doesnt come off as too new naive but due to the amount of links on the front page about ML - what is so fascinating about ML?? Why is there not the same level of interest/links on topics such as cryptology, graphics, circuits, comp architecture ?

deleted 15y ago
[deleted]
kaybe 15y ago
I find the potential fascinating. ML allows computers to solve problems without a human and become more powerful tools that can eventually act on their own. The extent to which this is possible is not clear yet, but is potentially very far-reaching and can be very empowering for human-kind, in all areas of our existence. The other topics you mentioned are interesting as well, of course, but their potential impact is much lower (although computer architecture can border on ML). (edit typo)
deleted 15y ago
[deleted]
law 15y ago
There's this enormous focus on 'web scale' technologies. This focus necessarily invokes visualizing and making sense of terabytes and eventually even petabytes of data; conventional approaches would take thousands or millions of man hours to accomplish the same level of analysis that computers can perform in hours or days. Tom Mitchell's definition of machine learning algorithms as those that improve their performance at some task with experience is precisely the way in which humans go about learning what's necessary to perform the same tasks that formerly took thousands or millions of hours. For highly dimensional problems, such as text classification (i.e., spam detection) or image classification (i.e., facial detection), it's almost impossible to hard code an algorithm to accomplish its goal without using machine learning. It's much easier to use a binary spam/not spam or face/not face labeling system that, given the attributes of the example, can learn which attributes beget that specific label. In other words, it's much easier for a learning system to determine what variables are important in the ultimate classification than trying to model the "true" function that gives rise to the labeling.

igrekel 15y ago

Cool. I'm disappointed that there isn't a video for hidden markov models and other models for time series tough, just slides. The schedule says that session is in march, maybe by then there will be a video online.

schiptsov 15y ago

Silverlight is required to use the Panopto viewer. - FUCK YOU!

kky 15y ago

I love that open source mentality (sharing and collaborating for the love of the work, community, and result) is reaching higher ed. I can't wait for it to reach lower ed! If kids start seeing this model at a young age...