5 ms·

Is this a good start for Math required for Machine Learning ?

jonnybgood 10y ago
Yes, discrete math is a good start for anything CS, but you'll definitely need more than what's presented here.
mwambua 10y ago
Not really. You're better off looking at introductory calculus and statistics. This book places more emphasis on discrete math. One good way to go about it is to audit an online course [like Andrew Ng's] and figure out what gaps you need to fill in your knowledge to understand the material.
jpau 10y ago
From what starting position are you asking from; what is your current math background?
- vayarajesh 10y ago
  I am familiar with Linear algebra and at most average understanding of graph theory. I know there is lot more math to cover for ML but I find it overwhelming to start
  - vecter 10y ago
    All you need for introductory machine learning is multivariable calculus (for some simple optimization stuff), linear algebra, and probability. If you don't know probability, here's a good course: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-041-probabilistic-systems-analysis-and-applied-probability-fall-2010/ https://ocw.mit.edu/courses/electrical-engineering-and-compu.... Once you feel comfortable with those, you'll be more than ready to tackle 6.867: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/ https://ocw.mit.edu/courses/electrical-engineering-and-compu....
  - jayajay 10y ago
    In its current state, even cutting-edge machine learning is pretty accessible if you have a good understanding of linear algebra and calculus. If you want to do have a deeper understanding of machine learning then vector calculus, tensors, graph theory, etc. can only help you.
__john 10y ago
Not really. The paper deals with "conventional" CS math. Here are some good resources for ML/DL math. https://hn.algolia.com/?query=machine%20learning%20math&sort=byPopularity&prefix&page=0&dateRange=all&type=story https://hn.algolia.com/?query=machine%20learning%20math&sort...
_qhtn 10y ago
Needs more: Linear Algebra: http://joshua.smcvt.edu/linearalgebra/ http://joshua.smcvt.edu/linearalgebra/ Calculus: https://cnx.org/contents/i4nRcikn@2.48:H2TLb2-S@2/Introduction https://cnx.org/contents/i4nRcikn@2.48:H2TLb2-S@2/Introducti... https://cnx.org/contents/HTmjSAcf@2.40:rrzms6rP@2/Introduction https://cnx.org/contents/HTmjSAcf@2.40:rrzms6rP@2/Introducti... https://cnx.org/contents/oxzXkyFi@2.49:72YaCFgv@2/Introduction https://cnx.org/contents/oxzXkyFi@2.49:72YaCFgv@2/Introducti...
- austenallred 10y ago
  I can't thank you enough for this comment
  - trentmb 10y ago
    I would be remiss if I didn't also link Paul's Online Math Notes. http://tutorial.math.lamar.edu/ http://tutorial.math.lamar.edu/ Also: /r/learnmath We're friendly!
    - llzes 10y ago
      You're a godsend, thank you from another person. I've been slowly self working through textbooks my friends give me over the years after they finish from their classes but haven't really known which direction to go in being nontraditional.
      - trentmb 10y ago
        Don't thank me- thank Jim Hefferon, Paul, and the OpenStax project for having the decency to make these materials available.
    - austenallred 10y ago
      Is there a recommended order to these? I skipped two years of math in High School and ended up BSing my way through Calculus without learning any of it, so I'm trying to figure out what I may need to fill the gaps
gumby 10y ago
No, this is for a general introduction to the mathematics of computer science. This looks like a basic (and good!) text any MIT freshman should be able to master. Perhaps it's for what 6.001 has morphed into? If you understand this stuff, you really need linear algebra for today's "deep learning", which perhaps is 18.03 (I can no longer remember).
- vecter 10y ago
  This class is 6.042 and is not required for CS majors. 6.001 morphed into 6.01, which is something to do with python. 18.06 is linear algebra. 18.03 is differential equations, which you don't really need for machine learning.
  - santaclaus 10y ago
    > 18.06 is linear algebra Is Gilbert Strang still teaching linear algebra at MIT? His intro materials to all things applied math are incredibly accessible.
    - vecter 10y ago
      No idea. He's definitely getting up there in years, so if he is still teaching now, I don't know how much longer it will be for. Strang doesn't teach 18.06 every semester anyway.
  - p4lindromica 10y ago
    6.042 fulfills a math requirement for 6-3 (CS) students. https://www.eecs.mit.edu/curriculum2016 https://www.eecs.mit.edu/curriculum2016
    - vecter 10y ago
      I meant Course 6, which can be 6-1 (EE) or 6-2 (EECS). Given that there are many 6-2 majors out there who say they've majored in computer science, it might come as a surprise to others who don't understand the MIT curriculum that a "computer science" major at MIT does not need to take this class.

Yes, discrete math is a good start for anything CS, but you'll definitely need more than what's presented here.

Not really. You're better off looking at introductory calculus and statistics. This book places more emphasis on discrete math. One good way to go about it is to audit an online course [like Andrew Ng's] and figure out what gaps you need to fill in your knowledge to understand the material.

jpau 10y ago

From what starting position are you asking from; what is your current math background?

vayarajesh 10y ago
I am familiar with Linear algebra and at most average understanding of graph theory. I know there is lot more math to cover for ML but I find it overwhelming to start
- vecter 10y ago
  All you need for introductory machine learning is multivariable calculus (for some simple optimization stuff), linear algebra, and probability. If you don't know probability, here's a good course: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-041-probabilistic-systems-analysis-and-applied-probability-fall-2010/ https://ocw.mit.edu/courses/electrical-engineering-and-compu.... Once you feel comfortable with those, you'll be more than ready to tackle 6.867: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/ https://ocw.mit.edu/courses/electrical-engineering-and-compu....
- jayajay 10y ago
  In its current state, even cutting-edge machine learning is pretty accessible if you have a good understanding of linear algebra and calculus. If you want to do have a deeper understanding of machine learning then vector calculus, tensors, graph theory, etc. can only help you.

__john 10y ago

Not really. The paper deals with "conventional" CS math. Here are some good resources for ML/DL math. https://hn.algolia.com/?query=machine%20learning%20math&sort=byPopularity&prefix&page=0&dateRange=all&type=story https://hn.algolia.com/?query=machine%20learning%20math&sort...

_qhtn 10y ago

Needs more: Linear Algebra: http://joshua.smcvt.edu/linearalgebra/ http://joshua.smcvt.edu/linearalgebra/ Calculus: https://cnx.org/contents/i4nRcikn@2.48:H2TLb2-S@2/Introduction https://cnx.org/contents/i4nRcikn@2.48:H2TLb2-S@2/Introducti... https://cnx.org/contents/HTmjSAcf@2.40:rrzms6rP@2/Introduction https://cnx.org/contents/HTmjSAcf@2.40:rrzms6rP@2/Introducti... https://cnx.org/contents/oxzXkyFi@2.49:72YaCFgv@2/Introduction https://cnx.org/contents/oxzXkyFi@2.49:72YaCFgv@2/Introducti...

austenallred 10y ago
I can't thank you enough for this comment
- trentmb 10y ago
  I would be remiss if I didn't also link Paul's Online Math Notes. http://tutorial.math.lamar.edu/ http://tutorial.math.lamar.edu/ Also: /r/learnmath We're friendly!
  - llzes 10y ago
    You're a godsend, thank you from another person. I've been slowly self working through textbooks my friends give me over the years after they finish from their classes but haven't really known which direction to go in being nontraditional.
    - trentmb 10y ago
      Don't thank me- thank Jim Hefferon, Paul, and the OpenStax project for having the decency to make these materials available.
  - austenallred 10y ago
    Is there a recommended order to these? I skipped two years of math in High School and ended up BSing my way through Calculus without learning any of it, so I'm trying to figure out what I may need to fill the gaps

gumby 10y ago

No, this is for a general introduction to the mathematics of computer science. This looks like a basic (and good!) text any MIT freshman should be able to master. Perhaps it's for what 6.001 has morphed into? If you understand this stuff, you really need linear algebra for today's "deep learning", which perhaps is 18.03 (I can no longer remember).

vecter 10y ago
This class is 6.042 and is not required for CS majors. 6.001 morphed into 6.01, which is something to do with python. 18.06 is linear algebra. 18.03 is differential equations, which you don't really need for machine learning.
- santaclaus 10y ago
  > 18.06 is linear algebra Is Gilbert Strang still teaching linear algebra at MIT? His intro materials to all things applied math are incredibly accessible.
  - vecter 10y ago
    No idea. He's definitely getting up there in years, so if he is still teaching now, I don't know how much longer it will be for. Strang doesn't teach 18.06 every semester anyway.
- p4lindromica 10y ago
  6.042 fulfills a math requirement for 6-3 (CS) students. https://www.eecs.mit.edu/curriculum2016 https://www.eecs.mit.edu/curriculum2016
  - vecter 10y ago
    I meant Course 6, which can be 6-1 (EE) or 6-2 (EECS). Given that there are many 6-2 majors out there who say they've majored in computer science, it might come as a surprise to others who don't understand the MIT curriculum that a "computer science" major at MIT does not need to take this class.