Which books /tutorials will be good for these topics for AI computer science student I have found from the internet that I need to know these topics for understanding Artificial Intelligence:

Matrix algebra: most machine learning models are represented as matrices and vectors. Concepts like eigenvectors and singular value
  decomposition appear all over the place.
Bayesian statistics: probability, Bayes' rule, common distributions (e.g., beta, Dirichlet, Gaussian), etc.
Multivariable calculus: most learning techniques use gradients and Hessians at their core to fit parameters. (If you want to get fancier,
  study numerical optimization.)
Information theory: entropy, KL divergence, etc. Just the basics here.
In limited cases, higher-level math can be useful. E.g., to understand
  manifold learning, you'll want to know some basic notions from
  geometry and topology. Occasionally abstract algebra is used (e.g.,
  see "expectation semirings" for learning on hyper-graphs). I would
  learn these as-needed, but if you have a chance to learn them early it
  can't hurt.

Now whenever I want to learn these I got confused with symbols, functions, vectors, sets, subsets, etc. Provided I know only the basic math, how can I learn those? I am confused which things should I learn first and which second.

We used "Pattern Classification" by "Duda, Hart and Stork". It is pretty good book.
Salahuddin
http://maths-on-line.blogspot.in/
