6 ms·

A Course in Machine Learning

229 points by sebg 13y ago

tga 13y ago
If you are interested in this you might want to also look at Andrew Ng's (Stanford) Machine Learning course that is starting soon on Coursera. https://www.coursera.org/course/ml https://www.coursera.org/course/ml
- kyro 13y ago
  Is this worth going through over picking up a textbook or two? I've found that Coursera courses are actually quite bloated. Lots and lots of empty talking, and very little substance.
  - Discordian93 13y ago
    Depends on the course, but other a than a select few, I tend to agree. Not the case with edX courses, though, I think they are up to a much higher standard. (At least the programming/math ones).
  - sampo 13y ago
    I've taken the Coursera ML class. It's very easy if you have the adequate math background. And it's not very comprehensive, there are lots of machine learning methods that are not covered. So it's more like an introductory course to machine learning. But it's absolutely commendable how Andrew Ng takes the topic to such an understandable level that a clever high schooler who knows a little about programming could take the course. There's even extra videos serving as a crash-course to linear algebra and octave programming in the first week. So he really manages to make the topic accessible to a very large audience.
    - dominotw 13y ago
      >adequate math background Do you know what kind of math is needed other than linear algebra.
      - Discordian93 13y ago
        Haven't done the course, but from the preview of the videos, they cover the math you need and it's just basic, high-school level knowledge of linear algebra.
      - sampo 13y ago
        Basic vector and matrix operations. The first half of a typical freshman linear algebra course is more than enough. But like I said, there is a matrix review in the beginning, so if you're willing to study those extra lectures, then almost no prior knowledge is needed. Also being able to take derivatives helps in a couple of places, but is not necessary.
        yelnatz 13y ago
        Thats linear algebra.
      - nrmn 13y ago
        Some calculus and linear algebra. The majority is linear algebra.
  - rm999 13y ago
    For a beginner to machine learning I'd recommend Andrew Ng's course notes and lectures over any textbook I've seen. But I prefer his Stanford CS 229 notes to Coursera for exactly the reasons you state: they are watered down. After you really can understand Andrew Ng's course notes I'd recommend a textbook because they go in more detail and cover more topics. My two favorites for general statistical machine learning are: * Pattern Recognition and Machine Learning by Christopher M. Bishop * The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman Both are very intensive, perhaps to a fault. But they are good references and are good to at least skim through after you have baseline machine learning knowledge. At this stage you should be able to read almost any machine learning paper and actually understand it.
    - reyan 13y ago
      Isn't Murphy's book more up to date and comprehensive as a reference? Edit: Andrew Ng's Coursera course is CS229A (http://cs229a.stanford.edu/ http://cs229a.stanford.edu/), not really watered down.
      - achompas 13y ago
        I'm a big fan of Murphy but its comprehensiveness means you lose some detailed explanations. Bishop really gets at those details (so does EoSL).
- denzil_correa 13y ago
  Here's a comment which I had written earlier on another article. The context was about learning ML with Python - of course the objective of Hal is more generic but some parts of it apply here too. https://news.ycombinator.com/item?id=5802968 https://news.ycombinator.com/item?id=5802968 The book details building ML systems with Python and does not necessarily teach ML per se. It is a good time to write a ML book in Python particularly keeping in mind efforts to make Python scale to Big Data [0]. What material you want to refer to is entirely dependent on What you want to do?. Here are some of my recommendations- Q : Do you want to have an "Introduction to ML", some applications with Octave/Matlab as your toolbox? A :Take up Andrew Ng's course on ML in Coursera [1]. Q : Do you want to have a complete understanding of ML with the mathematics, proofs and build your own algorithms in Octave/Matlab? A : Take up Andrew Ng's course on ML as taught in Stanford; video lectures are available for free download [2]. Note - This is NOT the same as the Coursera course. For textbook lovers, I have found the handouts distributed in this course far better than textbooks with obscure and esoteric terms. It is entirely self contained. If you want an alternate opinion, try out Yaser Abu-Mostafa's ML course at Caltech [3]. Q : Do you want to apply ML along with NLP using Python ? A : Try out Natural Language Tool Kit [4]. The HTML version of the NLTK book is freely available (Jump to Chapter 6 for the ML part) [5]. There is an NLTK cookbook available as well which has simple code examples to get you started [6]. Q: Do you want to apply standard ML algorithms using Python? A : Try out scikit-learn [7]. The OP's book also seems to be a good fit in this category (Disclaimer - I haven't read the OP's book and this is not an endorsement). [0] http://www.drdobbs.com/tools/us-defense-agency-feeds-python/.. http://www.drdobbs.com/tools/us-defense-agency-feeds-python/.... [1] https://www.coursera.org/course/ml https://www.coursera.org/course/ml [2] http://academicearth.org/courses/machine-learning/ http://academicearth.org/courses/machine-learning/ [3] http://work.caltech.edu/telecourse.html http://work.caltech.edu/telecourse.html [4] http://nltk.org http://nltk.org [5] http://nltk.org/book/ http://nltk.org/book/ [6] http://www.amazon.com/Python-Text-Processing-NLTK-Cookbook/d.. http://www.amazon.com/Python-Text-Processing-NLTK-Cookbook/d.... [7] http://scikit-learn.org http://scikit-learn.org
chatman 13y ago
The pages are watermarked, "Draft. Do not distribute". I wouldn't rely so much on these lectures unless they stabilize.
- winter_blue 13y ago
  Doesn't make them any less worth reading.
  - frozenport 13y ago
    If the content is wrong or suboptimal it makes them less worth reading. If the guy hasn't spent time thinking about the content of his slides it might be a confused and confusing jumble of nonsense: sometimes the best researchers are the worst teachers.
genofon 13y ago
please change the logo! I'm not trying to be jerky, it's always remarkable that someone wants to share his knowledge and I know probably it's not your priority, but the book and website will be better off without it. also why do you have the topics "introduction to machine learning" and "decision trees" in the same chapter?
Discordian93 13y ago
Edx is also offering the caltech ML course in mooc format: https://www.edx.org/course/caltechx/cs1156x/learning-data/1120 https://www.edx.org/course/caltechx/cs1156x/learning-data/11...
- mikevm 13y ago
  Note that this is not a watered-down course! It's the same course as the students at Caltech are taking, and in fact they will have the option not to attend the lectures and watch them online instead. The homework assignments will be the same ones as well (AFAIK).
- epsylon 13y ago
  I've taken the previous (non-edX) version and this is by far one of the best and clearest MOOC (and even offline course) I've taken.
Smerity 13y ago
For those who might not know, Hal Daumé III is a highly respected researcher in machine learning, spending a lot of time working in Natural Language Processing (NLP). He also contributes his knowledge to many interesting open source projects. After creating an algorithm called SEARN for solving hard structured prediction problems, Daumé et al. went and created a practical and open implementation of the algorithm in Vowpal Wabbit, an online machine learning library built for speed and used at Yahoo and Microsoft Research amongst other places. He's the sort of academic who contributes not just to theory, but also to practice. Browsing through his publications gives an idea of his contributions[1]. Given all that, I'm looking forward to his take on machine learning and for another valuable resource to be available to everyone. [1]: http://www.umiacs.umd.edu/~hal/publications.html http://www.umiacs.umd.edu/~hal/publications.html
- zenbowman 13y ago
  Nice. He was a fellow PhD student at USC/ISI, I remember him being the dude who always walked around barefoot. Seems like he's done really well since, I will definitely take a look at this.
- deleted 13y ago
  [deleted]
- mathattack 13y ago
  It looks very well organized, and that he's given thought to "How to teach" as well as "What to teach." Makes me wish I had 3 free hours a night over the next month. :-) OP - Thanks for sharing!
UK-AL 13y ago
I think he needs to re-run latex a couple times before releasing that pdf. Fair few ?? in there...
- adiM 13y ago
  The lines are not properly justified, so it appears that the output is not generated using TeX. I fail to see why would anyone choose left flushed alignment (with hyphenation!) when using TeX?
denzil_correa 13y ago
I wish more people followed what Hal writes in here : > A second goal of this book is to provide a view of machine learning that focuses on ideas and models, not on math. It is not possible (or even advisable) to avoid math. But math should be there to aid understanding, not hinder it. No book (yes not even PRML - Bishop), follows this diligently.
- rm999 13y ago
  This is a harder problem than it sounds, and something I've given a lot of thought to. I think the underlying issue is that all machine learning was discovered through a combination of applied math and intuitive ideas/models. Without the intuitive model no one would have thought to discover the method, and without the math the intuitive idea would be a pipe dream. Both are fundamentally linked, and it's a bad idea to separate them. For example, one of the most basic and oldest statistical methods is linear regression. The first thing anyone will tell you when they are teaching it is the basic "idea and model" - finding a line that fits a scatter plot (and then extending that idea). But this doesn't give you a real understanding of linear regression: where does that line come from, and why does a clean, algorithmic solution exist? Why does the standard solution often lead to numerical errors, and why is regularization a valid solution? These questions require an increasing amount of math, but they are essential to really understanding linear regression. I agree that some textbooks just throw you a wall of math, but many actually do a solid job of explaining what is going on as they do so. If you don't want to know the details of machine learning methods, which are inherently mathematical, you might as well as just remember the names of libraries that implement the solutions for you.
  - denzil_correa 13y ago
    You are right, it is indeed difficult and I never meant to say it is easy. My tripe was with the people who intentionally make it difficult. The thing is that you can learn ML with minimal (not minimum) math and that is what should be tried to achieved. I would be curious to know the textbooks you talk about - I have read quite a lot of those and struggled with them. For example - Linear regression is just brilliantly tackled by Andrew Ng in his ML course (CS229 at Stanford Lecture Notes - not Coursera).
leefrank 13y ago
my friend's mother-in-law makes $76/hr on the internet. She has been fired for 9 months but last month her payment was $14190 just working on the internet for a few hours. this big57.com
tedks 13y ago
Hal uses this as the textbook for his undergrad machine learning course. Having recently taken that course (in the spring 2013 semester), I feel justified saying that this book needs a lot of work before it's usable as a textbook or even a learning tool. Most of the time this text served primarily as an initial "dereference" of ideas yielding not concrete information, but a series of other pointers that I'd need to chase on Google before getting anything usable. He clearly has high hopes for CIML, based on all the infoboxes and chapters that are still incomplete; it's a shame he'll likely need to get tenure before finishing it. (This should in no way be taken as a slight on Hal's teaching; his class was the best sort of challenging in that it required a large amount of work, and yielded a correspondingly large amount of insight. He's also a fantastic lecturer and a fair grader.)
- ninjin 13y ago
  > "... it's a shame he'll likely need to get tenure before finishing it." Judging by his homepage [1] he is at least on the tenure track. Unless the notion of an Assistant Professor is different over in the states. I agree about the sentiment about the book, at least the chapter on Neural Networks needs some serious work (I believe there are errors in there, maybe I should mail Hal). As a person and researcher I only have good things to say about him, do check out it his blog if you are into ML and NLP [2] and let's hope he will find the time to continue polishing on this freely available book (I do like his writing). [1]: http://www.umiacs.umd.edu/~hal/ http://www.umiacs.umd.edu/~hal/ [2]: http://nlpers.blogspot.com/ http://nlpers.blogspot.com/
nrox 13y ago
The book seams accessible. This subject is difficult and requires a lot of time to understand and put something useful into practice. AI books appear frequently these days. I feel that most of books and tutorials fail to deliver good practical examples and meta-code, focusing more on mathematical proofs. I am struggling to understand Peter Abeel's aprenticeship learning: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.74.1035&rep=rep1&type=pdf http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.74.... He can resume the thing in a couple of slides. It seams short and simple (!?) but I think I am still away from truly understand it. Maybe next year.
genevievemp 13y ago
Just wanted to add another textbook option that's of comparable breadth and quality, but easier to read than bishop. There are pre-print pdfs probably available online with a little searching. The exercises and accompanying data are really fun imo. http://www.cs.ubc.ca/~murphyk/MLbook/index.html http://www.cs.ubc.ca/~murphyk/MLbook/index.html
- gtani 13y ago
  It's an excellent book, good balance of rigor and code/graphs/visuals to develop intuition, but pls see errata before buying http://www.cs.ubc.ca/~murphyk/MLbook/errata.html http://www.cs.ubc.ca/~murphyk/MLbook/errata.html (and the freely available content books by Barber, McKay and Hastie/Tibshirani/Friedman (ESL) are all worth close reads
mrcactu5 13y ago
this looks like a pleasant introduction to machine learning

229 points by sebg 13y ago

tga 13y ago

If you are interested in this you might want to also look at Andrew Ng's (Stanford) Machine Learning course that is starting soon on Coursera. https://www.coursera.org/course/ml https://www.coursera.org/course/ml

kyro 13y ago
Is this worth going through over picking up a textbook or two? I've found that Coursera courses are actually quite bloated. Lots and lots of empty talking, and very little substance.
- Discordian93 13y ago
  Depends on the course, but other a than a select few, I tend to agree. Not the case with edX courses, though, I think they are up to a much higher standard. (At least the programming/math ones).
- sampo 13y ago
  I've taken the Coursera ML class. It's very easy if you have the adequate math background. And it's not very comprehensive, there are lots of machine learning methods that are not covered. So it's more like an introductory course to machine learning. But it's absolutely commendable how Andrew Ng takes the topic to such an understandable level that a clever high schooler who knows a little about programming could take the course. There's even extra videos serving as a crash-course to linear algebra and octave programming in the first week. So he really manages to make the topic accessible to a very large audience.
  - dominotw 13y ago
    >adequate math background Do you know what kind of math is needed other than linear algebra.
    - Discordian93 13y ago
      Haven't done the course, but from the preview of the videos, they cover the math you need and it's just basic, high-school level knowledge of linear algebra.
    - sampo 13y ago
      Basic vector and matrix operations. The first half of a typical freshman linear algebra course is more than enough. But like I said, there is a matrix review in the beginning, so if you're willing to study those extra lectures, then almost no prior knowledge is needed. Also being able to take derivatives helps in a couple of places, but is not necessary.
      - yelnatz 13y ago
        Thats linear algebra.
    - nrmn 13y ago
      Some calculus and linear algebra. The majority is linear algebra.
- rm999 13y ago
  For a beginner to machine learning I'd recommend Andrew Ng's course notes and lectures over any textbook I've seen. But I prefer his Stanford CS 229 notes to Coursera for exactly the reasons you state: they are watered down. After you really can understand Andrew Ng's course notes I'd recommend a textbook because they go in more detail and cover more topics. My two favorites for general statistical machine learning are: * Pattern Recognition and Machine Learning by Christopher M. Bishop * The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman Both are very intensive, perhaps to a fault. But they are good references and are good to at least skim through after you have baseline machine learning knowledge. At this stage you should be able to read almost any machine learning paper and actually understand it.
  - reyan 13y ago
    Isn't Murphy's book more up to date and comprehensive as a reference? Edit: Andrew Ng's Coursera course is CS229A (http://cs229a.stanford.edu/ http://cs229a.stanford.edu/), not really watered down.
    - achompas 13y ago
      I'm a big fan of Murphy but its comprehensiveness means you lose some detailed explanations. Bishop really gets at those details (so does EoSL).
denzil_correa 13y ago
Here's a comment which I had written earlier on another article. The context was about learning ML with Python - of course the objective of Hal is more generic but some parts of it apply here too. https://news.ycombinator.com/item?id=5802968 https://news.ycombinator.com/item?id=5802968 The book details building ML systems with Python and does not necessarily teach ML per se. It is a good time to write a ML book in Python particularly keeping in mind efforts to make Python scale to Big Data [0]. What material you want to refer to is entirely dependent on What you want to do?. Here are some of my recommendations- Q : Do you want to have an "Introduction to ML", some applications with Octave/Matlab as your toolbox? A :Take up Andrew Ng's course on ML in Coursera [1]. Q : Do you want to have a complete understanding of ML with the mathematics, proofs and build your own algorithms in Octave/Matlab? A : Take up Andrew Ng's course on ML as taught in Stanford; video lectures are available for free download [2]. Note - This is NOT the same as the Coursera course. For textbook lovers, I have found the handouts distributed in this course far better than textbooks with obscure and esoteric terms. It is entirely self contained. If you want an alternate opinion, try out Yaser Abu-Mostafa's ML course at Caltech [3]. Q : Do you want to apply ML along with NLP using Python ? A : Try out Natural Language Tool Kit [4]. The HTML version of the NLTK book is freely available (Jump to Chapter 6 for the ML part) [5]. There is an NLTK cookbook available as well which has simple code examples to get you started [6]. Q: Do you want to apply standard ML algorithms using Python? A : Try out scikit-learn [7]. The OP's book also seems to be a good fit in this category (Disclaimer - I haven't read the OP's book and this is not an endorsement). [0] http://www.drdobbs.com/tools/us-defense-agency-feeds-python/.. http://www.drdobbs.com/tools/us-defense-agency-feeds-python/.... [1] https://www.coursera.org/course/ml https://www.coursera.org/course/ml [2] http://academicearth.org/courses/machine-learning/ http://academicearth.org/courses/machine-learning/ [3] http://work.caltech.edu/telecourse.html http://work.caltech.edu/telecourse.html [4] http://nltk.org http://nltk.org [5] http://nltk.org/book/ http://nltk.org/book/ [6] http://www.amazon.com/Python-Text-Processing-NLTK-Cookbook/d.. http://www.amazon.com/Python-Text-Processing-NLTK-Cookbook/d.... [7] http://scikit-learn.org http://scikit-learn.org

chatman 13y ago

The pages are watermarked, "Draft. Do not distribute". I wouldn't rely so much on these lectures unless they stabilize.

winter_blue 13y ago
Doesn't make them any less worth reading.
- frozenport 13y ago
  If the content is wrong or suboptimal it makes them less worth reading. If the guy hasn't spent time thinking about the content of his slides it might be a confused and confusing jumble of nonsense: sometimes the best researchers are the worst teachers.

genofon 13y ago

please change the logo! I'm not trying to be jerky, it's always remarkable that someone wants to share his knowledge and I know probably it's not your priority, but the book and website will be better off without it. also why do you have the topics "introduction to machine learning" and "decision trees" in the same chapter?

Discordian93 13y ago

Edx is also offering the caltech ML course in mooc format: https://www.edx.org/course/caltechx/cs1156x/learning-data/1120 https://www.edx.org/course/caltechx/cs1156x/learning-data/11...

mikevm 13y ago
Note that this is not a watered-down course! It's the same course as the students at Caltech are taking, and in fact they will have the option not to attend the lectures and watch them online instead. The homework assignments will be the same ones as well (AFAIK).
epsylon 13y ago
I've taken the previous (non-edX) version and this is by far one of the best and clearest MOOC (and even offline course) I've taken.

Smerity 13y ago

For those who might not know, Hal Daumé III is a highly respected researcher in machine learning, spending a lot of time working in Natural Language Processing (NLP). He also contributes his knowledge to many interesting open source projects. After creating an algorithm called SEARN for solving hard structured prediction problems, Daumé et al. went and created a practical and open implementation of the algorithm in Vowpal Wabbit, an online machine learning library built for speed and used at Yahoo and Microsoft Research amongst other places. He's the sort of academic who contributes not just to theory, but also to practice. Browsing through his publications gives an idea of his contributions[1]. Given all that, I'm looking forward to his take on machine learning and for another valuable resource to be available to everyone. [1]: http://www.umiacs.umd.edu/~hal/publications.html http://www.umiacs.umd.edu/~hal/publications.html

zenbowman 13y ago
Nice. He was a fellow PhD student at USC/ISI, I remember him being the dude who always walked around barefoot. Seems like he's done really well since, I will definitely take a look at this.
deleted 13y ago
[deleted]
mathattack 13y ago
It looks very well organized, and that he's given thought to "How to teach" as well as "What to teach." Makes me wish I had 3 free hours a night over the next month. :-) OP - Thanks for sharing!

UK-AL 13y ago

I think he needs to re-run latex a couple times before releasing that pdf. Fair few ?? in there...

adiM 13y ago
The lines are not properly justified, so it appears that the output is not generated using TeX. I fail to see why would anyone choose left flushed alignment (with hyphenation!) when using TeX?

denzil_correa 13y ago

I wish more people followed what Hal writes in here : > A second goal of this book is to provide a view of machine learning that focuses on ideas and models, not on math. It is not possible (or even advisable) to avoid math. But math should be there to aid understanding, not hinder it. No book (yes not even PRML - Bishop), follows this diligently.

rm999 13y ago
This is a harder problem than it sounds, and something I've given a lot of thought to. I think the underlying issue is that all machine learning was discovered through a combination of applied math and intuitive ideas/models. Without the intuitive model no one would have thought to discover the method, and without the math the intuitive idea would be a pipe dream. Both are fundamentally linked, and it's a bad idea to separate them. For example, one of the most basic and oldest statistical methods is linear regression. The first thing anyone will tell you when they are teaching it is the basic "idea and model" - finding a line that fits a scatter plot (and then extending that idea). But this doesn't give you a real understanding of linear regression: where does that line come from, and why does a clean, algorithmic solution exist? Why does the standard solution often lead to numerical errors, and why is regularization a valid solution? These questions require an increasing amount of math, but they are essential to really understanding linear regression. I agree that some textbooks just throw you a wall of math, but many actually do a solid job of explaining what is going on as they do so. If you don't want to know the details of machine learning methods, which are inherently mathematical, you might as well as just remember the names of libraries that implement the solutions for you.
- denzil_correa 13y ago
  You are right, it is indeed difficult and I never meant to say it is easy. My tripe was with the people who intentionally make it difficult. The thing is that you can learn ML with minimal (not minimum) math and that is what should be tried to achieved. I would be curious to know the textbooks you talk about - I have read quite a lot of those and struggled with them. For example - Linear regression is just brilliantly tackled by Andrew Ng in his ML course (CS229 at Stanford Lecture Notes - not Coursera).

leefrank 13y ago

my friend's mother-in-law makes $76/hr on the internet. She has been fired for 9 months but last month her payment was $14190 just working on the internet for a few hours. this big57.com

tedks 13y ago

Hal uses this as the textbook for his undergrad machine learning course. Having recently taken that course (in the spring 2013 semester), I feel justified saying that this book needs a lot of work before it's usable as a textbook or even a learning tool. Most of the time this text served primarily as an initial "dereference" of ideas yielding not concrete information, but a series of other pointers that I'd need to chase on Google before getting anything usable. He clearly has high hopes for CIML, based on all the infoboxes and chapters that are still incomplete; it's a shame he'll likely need to get tenure before finishing it. (This should in no way be taken as a slight on Hal's teaching; his class was the best sort of challenging in that it required a large amount of work, and yielded a correspondingly large amount of insight. He's also a fantastic lecturer and a fair grader.)

ninjin 13y ago
> "... it's a shame he'll likely need to get tenure before finishing it." Judging by his homepage [1] he is at least on the tenure track. Unless the notion of an Assistant Professor is different over in the states. I agree about the sentiment about the book, at least the chapter on Neural Networks needs some serious work (I believe there are errors in there, maybe I should mail Hal). As a person and researcher I only have good things to say about him, do check out it his blog if you are into ML and NLP [2] and let's hope he will find the time to continue polishing on this freely available book (I do like his writing). [1]: http://www.umiacs.umd.edu/~hal/ http://www.umiacs.umd.edu/~hal/ [2]: http://nlpers.blogspot.com/ http://nlpers.blogspot.com/

nrox 13y ago

The book seams accessible. This subject is difficult and requires a lot of time to understand and put something useful into practice. AI books appear frequently these days. I feel that most of books and tutorials fail to deliver good practical examples and meta-code, focusing more on mathematical proofs. I am struggling to understand Peter Abeel's aprenticeship learning: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.74.1035&rep=rep1&type=pdf http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.74.... He can resume the thing in a couple of slides. It seams short and simple (!?) but I think I am still away from truly understand it. Maybe next year.

genevievemp 13y ago

Just wanted to add another textbook option that's of comparable breadth and quality, but easier to read than bishop. There are pre-print pdfs probably available online with a little searching. The exercises and accompanying data are really fun imo. http://www.cs.ubc.ca/~murphyk/MLbook/index.html http://www.cs.ubc.ca/~murphyk/MLbook/index.html

gtani 13y ago
It's an excellent book, good balance of rigor and code/graphs/visuals to develop intuition, but pls see errata before buying http://www.cs.ubc.ca/~murphyk/MLbook/errata.html http://www.cs.ubc.ca/~murphyk/MLbook/errata.html (and the freely available content books by Barber, McKay and Hastie/Tibshirani/Friedman (ESL) are all worth close reads

mrcactu5 13y ago

this looks like a pleasant introduction to machine learning