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In my undergraduate course I learnt introductory level statistics and I really enjoyed it. With that background I decided to follow predictive analysis further and decided to take up this book . But I am really struggling with this as the math and probability part seems beyond my reach at the moment.

I am well aware with undergraduate level linear algebra (vector space, decomposition, linear transformation etc), undergraduate level probability and statistics ( one dimensional random variables, one dimensional parameter estimates, simple hypothesis testing, model verification on 1-D data etc), and introductory calculus (scalar valued and up to 3 variables).

I have been jumping on different books and resources to progress further but every time comes a new requirement while learning. (For example: topology while learning calculus on manifolds, optimization while learning classifications). I feel like I am going nowhere and just wasting time.

I hope someone will guide me with a strategy (books and resources and the order I should follow them in) for smooth transition from my now possessed knowledge to the level where I can finish the mentioned book with a thorough understanding and will be able to deal with exercises as well.

Thank you in advance!

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    $\begingroup$ There is an easier book by Hastie et al called "An Introduction to Statistical Learning" which is a friendly introduction to the material in "The Elements of Statistical Learning". (One of the authors of ISL is Daniela Witten, who is Ed Witten's daughter.) You don't need to know anything about Calculus on Manifolds to learn the material in ESL. Regarding optimization, you can go a long way just by understanding gradient descent and stochastic gradient descent. You might take a look at Strang's new book Linear Algebra and Learning from Data. $\endgroup$ – littleO May 15 at 9:20
  • $\begingroup$ @littleO Thanks a lot. Both ISL and Strang's book really look promising and manageable read based on contents. I will give them a try. $\endgroup$ – mw981 May 15 at 10:50

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