title: How should I start with learning math required for AI
body: <p>I have studied mathematics, but that was long time ago. I have been a programmer for 8 years but when I started to study concepts in AI and data mining I find it very difficult to understand the theory.</p>
  
  <p>Now I have wasted 2-3 years and I have got nothing.  I need to first understand the math concepts required to learn AI and data mining. </p>
  
  <p>I don't know where to start.  Which books and tutorials do you recommend I should start with from the AI point of view.</p>
  
  <p>How should I go about obtaining the fundamental requirements to use AI and Data Mining concepts.</p>
  
  <p>EDIT:
  I got this list from internet</p>
  
  <p><strong>Matrix algebra:</strong> most machine learning models are represented as matrices and vectors. Concepts like eigenvectors and singular value decomposition appear all over the place.</p>
  
  <p><strong>Bayesian statistics:</strong> probability, Bayes' rule, common distributions (e.g., beta, Dirichlet, Gaussian), etc.</p>
  
  <p><strong>Multivariable calculus</strong>: most learning techniques use gradients and Hessians at their core to fit parameters. (If you want to get fancier, study numerical optimization.)</p>
  
  <p><strong>Information theory</strong>: entropy, KL divergence, etc. Just the basics here.</p>
  
  <p>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.</p>
  
  <p>Can anyone recommend some books on those</p>