MIT maths for artificial intelligence

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SUMMARY

To effectively learn artificial intelligence, foundational knowledge in mathematics is essential, but the focus should shift towards programming and computer science. The discussion highlights MIT's 18.01 Calculus course as a starting point, but emphasizes that more advanced mathematics, such as that covered in 18.014, may not be necessary. Instead, practical application through programming is crucial, with recommendations to study M. Bishop's "Pattern Recognition and Machine Learning" to gain insights into the mathematical concepts required, such as linear algebra and nonlinear optimization.

PREREQUISITES
  • Understanding of MIT 18.01 Calculus concepts
  • Familiarity with linear algebra
  • Basic knowledge of programming
  • Awareness of machine learning principles
NEXT STEPS
  • Study M. Bishop's "Pattern Recognition and Machine Learning"
  • Learn linear algebra techniques applicable to AI
  • Practice programming with Python for machine learning
  • Explore nonlinear optimization methods
USEFUL FOR

Individuals interested in artificial intelligence, particularly those transitioning from mathematics to programming, as well as students seeking practical applications of mathematical concepts in AI development.

SW24
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I want to learn artificial intelligence, so I decided to learn maths. I began from one-variable calculus at MIT open course ware. And these lectures seemed to me very easy, especially compared with Apostol's or Spivak's calculus.
So I want to ask is it enough MIT open course ware lectures for my goals or I should to learn maths with more hard literature.

Update.
Oh, of course i talked about 18.01, not about 18.014.
So the question is: do I need maths similar to 18.01 for learning artificial intelligence or I need more hard 18.014 like maths?
 
Last edited:
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If you want to learn artificial intelligence, math is not the right field to study. You need programming and computer science. Just get a book on the subject, e.g. M. Bishops's excellent "Pattern Recognition and Machine Learning", and code up and use the approaches discussed there in various trial tasks. The amount and kind of math you need will become clear during this process (e.g. linear algebra and nonlinear optimization). Learning good programming will be the main challenge in practice.

btw: we do not bump on this forum.
 
Where are you at in your education?
 

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