Archive for October, 2011

  Visualize  your data Our quest is supervised learning is to find function f(x) that is likely to generate your training set. The training set is defined by in which the input X, has output Y labels attached to them. One thing you learn quickly is the importance of analysing you data. There are some […]


See the 2 previous posts about the VXX estimation. We contended that there is only 1 parameter: the number of days we look back to get training samples. To be frank however, there are more parameters: –          whether selecting 1D or 2D case; or –          the machine learning algorithm used: (Normal Equation or Gradient Descent, […]


As the continuation of the previous post, let’s study the Linear Regression in which we have not only 1, but 2 variables. Let’s assume we want to forecast the next day %change of VXX as an output variable, based on the today %change of the VXX and the yesterday %change The linear equation would look […]


An advantage of attending a university course is that it broadens the knowledge someone have. But even more important to that, it adds new usable tools into the repertoires that we keep in our toolbox. The Stanford University Machine Learning course mentioned in the previous blog post is not only theoretical, but very practical indeed. […]


I would like to draw your attention to a unique Stanford University initiative. In this season, the first time ever, you can participate in a unique research project that intends to change the future of the education. Stanford University has announced to make 2 courses available online Worldwide! -Introduction to Artificial Intelligence -Machine Learning An […]