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Can anyone recommend a good intro in machine learning that does teaches the building blocks of the math side of things? It is fascinating as a topic, but there

Can anyone recommend a good intro in machine learning that does teaches the building blocks of the math side of things? It is fascinating as a topic, but there is such a large prerequisite learning curve that it seems out of reach for those not as strong in math. That might just be the reality of it, but hoping there might be a better introduction (even something super simple like a Codecademy equivalent).

jliechti1 12y ago
My guess is that you won't find any course that explains all the prerequisite math. It's probably more useful to build a solid foundation in probability theory (and therefore calculus) before going on. For machine learning, a good place to start is Andrew Ng's course on Coursera: https://www.coursera.org/course/ml https://www.coursera.org/course/ml It's pretty light on math, while at the same time giving you experience in implementing and understanding these techniques. From there, I might recommend Learning from Data and the associated video lectures: https://work.caltech.edu/telecourse.html https://work.caltech.edu/telecourse.html It is a bit of a jump, but it is a great course in presenting the field of machine learning and explaining the mathematical and statistical underpinnings in a systematic way.
- jamra 12y ago
  I just finished the Coursera course by Andrew Ng. It was great. The only hand waving done with math was when calculus was necessary. You can take some extra time to do that work yourself if you like, but you will not be missing the underpinnings of why things work statistically. The introduction to neural networks what finally gave me that aha moment. It is a very self contained course that is quite easy to follow. You can skip the programming exercises if you don't have the time.
  - crazypyro 12y ago
    For anyone interested in more about the specific math of neural networks, http://www.iro.umontreal.ca/~bengioy/dlbook http://www.iro.umontreal.ca/~bengioy/dlbook has a couple good introductory chapters that give overviews of most of the necessary topics for NNs, but also provides additional resource suggestions if you need more in-depth info on a certain subject.
coldnebo 12y ago
It doesn't cover machine learning in a lot of detail (there is one dogs and cats example) but J. Nathan Kutz book is an excellent introduction to the math behind this and other data modeling techniques with lots of hands on examples. The best thing about this book is that it is a broad survey of the maths you need in Data Science -- you get a feel for how various subjects fit together from a high level with practical examples, then you can branch out to other sources to learn more about specific methods. * http://www.amazon.com/Data-Driven-Modeling-Scientific-Computation-Methods/dp/0199660344 http://www.amazon.com/Data-Driven-Modeling-Scientific-Comput...
denzil_correa 12y ago
I have found Andrew Ng's math handouts for CS229 Stanford (not Coursera) the best. There's necessary introduction and abstraction of irrelevant details to the topic at hand. http://cs229.stanford.edu http://cs229.stanford.edu
drcomputer 12y ago
There is a large mathematical foundation behind machine learning, that is not even taught to most computer science students. The concepts that built machine learning are often found in engineering and mathematics: It's not easy to learn, especially if you are not strong in math, but if you want an intuitive understanding as to how machine learning works, I would recommend learning a combination of probability, linear algebra, and formal theories of computation (abstract machines): This book was really what opened the gate of tying the content together: http://www.amazon.com/First-Course-Probability-9th-Edition/dp/032179477X http://www.amazon.com/First-Course-Probability-9th-Edition/d... You should also be strong in discrete mathematics.
atrilla 12y ago
A very interesting requirement. Thanks for noting it. I'm setting up a blog on Artificial Intelligence (which inherently includes Machine Learning) focused on the contents of the AIMA book (by Stuart Russell and Peter Norvig): http://ai-maker.com/ http://ai-maker.com/ How would you like it to be? Code will be developed in Matlab, so the math depth is rather convenient using this tool.
Houshalter 12y ago
http://www.metacademy.org/ http://www.metacademy.org/ is a great source. It tells you what all the prerequisites are for everything as well as where you can learn them and what their prerequisites are and so on.
ilzmastr 12y ago
I was in a similar situation a year ago. Two things fixed my problems: I took a course on Linear Algebra (Bretscher's book up to chap 9) and a Probability course (Ross' book up to chap 6) and did very many problems by hand on paper. I just finished a ML course (Bishop's book, and Jordan's book), mixed grad+undergrad, which was 80% problems w/ pen and paper and 20% code up something algorithmically trivial but mathematically challenging, and don't think I would've been able to pick up the additional math along the way without these two great books and their many exercise problems behind me. I read layman's explanations of ML concepts a year ago, and got nowhere in terms of my own implementation+debugging/improvement upon established techniques. Now I can solve problems I saw a year ago and thought "no one can do this." My advice is to take the gateway drugs first, Probability (Ross <- I love this book!) and Lin Alg (I like Bretscher much better than Strang, but not everyone agrees with me :) Take a course in real life (for a grade and a transcript) at a competitive university if possible, nothing makes you study thoroughly like a gun to your head.
- avinassh 12y ago
  I would appreciate if you can give links to those books on Amazon or some other site. Thanks!
  - craigching 12y ago
    I was interested so I looked them up: A First Course in Probability: http://www.amazon.com/First-Course-Probability-9th/dp/032179477X/ref=sr_1_1?ie=UTF8&qid=1418999863&sr=8-1&keywords=probability+ross&pebp=1418999876886 http://www.amazon.com/First-Course-Probability-9th/dp/032179... Linear Algebra with Applications: http://www.amazon.com/Linear-Algebra-Applications-Otto-Bretscher/dp/0321796977/ref=sr_1_1?ie=UTF8&qid=1418999936&sr=8-1&keywords=linear+algebra+bretscher&pebp=1418999943552 http://www.amazon.com/Linear-Algebra-Applications-Otto-Brets... Note that the Bretscher book has really terrible reviews, but I can't evaluate if the reviews are correct or not.
    - Plough_Jogger 12y ago
      A First Course in Probability: http://www.ebay.com/itm/A-First-Course-in-Probability-EDN-9-by-Sheldon-Ross-/261607443332?pt=US_Texbook_Education&hash=item3ce904cb84 http://www.ebay.com/itm/A-First-Course-in-Probability-EDN-9-... Linear Algebra with Applications: http://www.ebay.com/itm/Linear-Algebra-with-Applications-5ed-by-Otto-Bretscher-/251574886499?pt=US_Texbook_Education&hash=item3a93082063 http://www.ebay.com/itm/Linear-Algebra-with-Applications-5ed... International editions of textbooks are immensely cheaper.
twelfthnight 12y ago
I think this has to do with learning styles, but I've found that working on real problems (like those on Kaggle) is a better way to learn machine learning than reading text books. When I'm working on problems, it becomes evident what I don't know. Then I'm able to intelligently go through the books and learn the relevant bits. When I start with the math, I tend not to remember anything because I have no foundation to attach the math knowledge to.
sonabinu 12y ago
This is a book from MIT press not quiet yet finished but getting there - might be a good source for material for getting the linear algebra and the probability pieces! Core material is Deep Learning http://www.iro.umontreal.ca/~bengioy/dlbook/ http://www.iro.umontreal.ca/~bengioy/dlbook/

jliechti1 12y ago

My guess is that you won't find any course that explains all the prerequisite math. It's probably more useful to build a solid foundation in probability theory (and therefore calculus) before going on. For machine learning, a good place to start is Andrew Ng's course on Coursera: https://www.coursera.org/course/ml https://www.coursera.org/course/ml It's pretty light on math, while at the same time giving you experience in implementing and understanding these techniques. From there, I might recommend Learning from Data and the associated video lectures: https://work.caltech.edu/telecourse.html https://work.caltech.edu/telecourse.html It is a bit of a jump, but it is a great course in presenting the field of machine learning and explaining the mathematical and statistical underpinnings in a systematic way.

jamra 12y ago
I just finished the Coursera course by Andrew Ng. It was great. The only hand waving done with math was when calculus was necessary. You can take some extra time to do that work yourself if you like, but you will not be missing the underpinnings of why things work statistically. The introduction to neural networks what finally gave me that aha moment. It is a very self contained course that is quite easy to follow. You can skip the programming exercises if you don't have the time.
- crazypyro 12y ago
  For anyone interested in more about the specific math of neural networks, http://www.iro.umontreal.ca/~bengioy/dlbook http://www.iro.umontreal.ca/~bengioy/dlbook has a couple good introductory chapters that give overviews of most of the necessary topics for NNs, but also provides additional resource suggestions if you need more in-depth info on a certain subject.

coldnebo 12y ago

It doesn't cover machine learning in a lot of detail (there is one dogs and cats example) but J. Nathan Kutz book is an excellent introduction to the math behind this and other data modeling techniques with lots of hands on examples. The best thing about this book is that it is a broad survey of the maths you need in Data Science -- you get a feel for how various subjects fit together from a high level with practical examples, then you can branch out to other sources to learn more about specific methods. * http://www.amazon.com/Data-Driven-Modeling-Scientific-Computation-Methods/dp/0199660344 http://www.amazon.com/Data-Driven-Modeling-Scientific-Comput...

denzil_correa 12y ago

I have found Andrew Ng's math handouts for CS229 Stanford (not Coursera) the best. There's necessary introduction and abstraction of irrelevant details to the topic at hand. http://cs229.stanford.edu http://cs229.stanford.edu

drcomputer 12y ago

There is a large mathematical foundation behind machine learning, that is not even taught to most computer science students. The concepts that built machine learning are often found in engineering and mathematics: It's not easy to learn, especially if you are not strong in math, but if you want an intuitive understanding as to how machine learning works, I would recommend learning a combination of probability, linear algebra, and formal theories of computation (abstract machines): This book was really what opened the gate of tying the content together: http://www.amazon.com/First-Course-Probability-9th-Edition/dp/032179477X http://www.amazon.com/First-Course-Probability-9th-Edition/d... You should also be strong in discrete mathematics.

atrilla 12y ago

A very interesting requirement. Thanks for noting it. I'm setting up a blog on Artificial Intelligence (which inherently includes Machine Learning) focused on the contents of the AIMA book (by Stuart Russell and Peter Norvig): http://ai-maker.com/ http://ai-maker.com/ How would you like it to be? Code will be developed in Matlab, so the math depth is rather convenient using this tool.

Houshalter 12y ago

http://www.metacademy.org/ http://www.metacademy.org/ is a great source. It tells you what all the prerequisites are for everything as well as where you can learn them and what their prerequisites are and so on.

ilzmastr 12y ago

I was in a similar situation a year ago. Two things fixed my problems: I took a course on Linear Algebra (Bretscher's book up to chap 9) and a Probability course (Ross' book up to chap 6) and did very many problems by hand on paper. I just finished a ML course (Bishop's book, and Jordan's book), mixed grad+undergrad, which was 80% problems w/ pen and paper and 20% code up something algorithmically trivial but mathematically challenging, and don't think I would've been able to pick up the additional math along the way without these two great books and their many exercise problems behind me. I read layman's explanations of ML concepts a year ago, and got nowhere in terms of my own implementation+debugging/improvement upon established techniques. Now I can solve problems I saw a year ago and thought "no one can do this." My advice is to take the gateway drugs first, Probability (Ross <- I love this book!) and Lin Alg (I like Bretscher much better than Strang, but not everyone agrees with me :) Take a course in real life (for a grade and a transcript) at a competitive university if possible, nothing makes you study thoroughly like a gun to your head.

avinassh 12y ago
I would appreciate if you can give links to those books on Amazon or some other site. Thanks!
- craigching 12y ago
  I was interested so I looked them up: A First Course in Probability: http://www.amazon.com/First-Course-Probability-9th/dp/032179477X/ref=sr_1_1?ie=UTF8&qid=1418999863&sr=8-1&keywords=probability+ross&pebp=1418999876886 http://www.amazon.com/First-Course-Probability-9th/dp/032179... Linear Algebra with Applications: http://www.amazon.com/Linear-Algebra-Applications-Otto-Bretscher/dp/0321796977/ref=sr_1_1?ie=UTF8&qid=1418999936&sr=8-1&keywords=linear+algebra+bretscher&pebp=1418999943552 http://www.amazon.com/Linear-Algebra-Applications-Otto-Brets... Note that the Bretscher book has really terrible reviews, but I can't evaluate if the reviews are correct or not.
  - Plough_Jogger 12y ago
    A First Course in Probability: http://www.ebay.com/itm/A-First-Course-in-Probability-EDN-9-by-Sheldon-Ross-/261607443332?pt=US_Texbook_Education&hash=item3ce904cb84 http://www.ebay.com/itm/A-First-Course-in-Probability-EDN-9-... Linear Algebra with Applications: http://www.ebay.com/itm/Linear-Algebra-with-Applications-5ed-by-Otto-Bretscher-/251574886499?pt=US_Texbook_Education&hash=item3a93082063 http://www.ebay.com/itm/Linear-Algebra-with-Applications-5ed... International editions of textbooks are immensely cheaper.

twelfthnight 12y ago

I think this has to do with learning styles, but I've found that working on real problems (like those on Kaggle) is a better way to learn machine learning than reading text books. When I'm working on problems, it becomes evident what I don't know. Then I'm able to intelligently go through the books and learn the relevant bits. When I start with the math, I tend not to remember anything because I have no foundation to attach the math knowledge to.

sonabinu 12y ago

This is a book from MIT press not quiet yet finished but getting there - might be a good source for material for getting the linear algebra and the probability pieces! Core material is Deep Learning http://www.iro.umontreal.ca/~bengioy/dlbook/ http://www.iro.umontreal.ca/~bengioy/dlbook/