Probability basics for machine learning [closed]

Question

I have recently started studying Machine Learning and found that I need to refresh probability basics such as Conditional Probability, Bayes Theorem etc.

I am looking for online resources where I can quickly brush up probability concepts wrt Machine Learning.

The online resorces, I stumbled upon are either very basic or too advanced.

Answer 1

This might help: http://www.cs.cmu.edu/~tom/10601_fall2012/lectures.shtml

The above link is from Tom Mitchell's Machine Learning Class @ CMU. Videos are available too. You will gain a very good understanding of ML concepts if you go through all the videos. (or just the first few videos for Conditional Probability, Bayes Theorem, etc).

Answer 2

The notion of conditional probability and bayes theorem are very basic themselves. It doesn't get any more basic than that in probabilistic modeling, you might say. Which suggests that you didn't look two well at what you've found or didn't really do any search at all.

Off the top of my head, I can name two resources: first, any Coursera course dealing with probabilities or machine learning (see AI, Statistics One or Probabilistic Graphical Models) contains these preliminaries. Second, there's a number of books on statistics freely available online, one example being Information Theory, Inference, and Learning Algorithms.