I am trying to learn machine learning and as a novice in the field of statistics, I am having a hard time while trying to read pattern recognition and machine learning by Bishop. I have already watched Andrew Ng's course on machine learning but now I want to get a mathematical point of view .

My problem is that I don't understand many gaussian processes mentioned in the book such as bayesian inference of gaussian , conditional and marginal gaussian ,etc. I have already tried to search for some resources but found none which were to the point.

So, is there any good resource which covers mathematical background (specially probability and distributions) required for machine learning ?


Introduction to Machine Learning by Ethem Alpaydin is a good start for machine learning before reading the Bishop's book.The book of Allen B. Downey Think Stats: Probability and Statistics for Programmers covers basic statistics.For Gaussian processes, I used to watch the lectures of Nando de Freitas on youtube.He explains the GP excellently.Keep in mind that I'm still a student, not an expert.

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  • $\begingroup$ Can you tell me how exactly "introduction to machine learning" and "pattern recognition and machine learning" differ ? I have checked and found out that they both cover similar topics. $\endgroup$ – Saksham Mar 17 '17 at 11:21
  • $\begingroup$ The Bishop's book is more detailed, covers more topics related to probabilistic methods of machine leraning and required more background information depending on your previous knowledge of the field. $\endgroup$ – Animate_Ant Mar 17 '17 at 13:21

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