Am in the midst of finishing the Stanford Machine Learning by Prof Andrew Ng. Need to read this soon though
Recently I got to read up on machine learning, more of introductory and elementary stuff. The more I read, the more similarity (and of course differences as well) I found as compared to statistics. This post I found (Statistics vs. Machine Learning, fight!) shed more light on the two topics.
Pattern Recognition, Neural Networks, Machine Learning, Graphical Models, Data Visualization, Big Data, Distributed Data Analysis, Parallel Computing… all seem to be related “technology”. The core interest, however, is data analysis:
The field should be defined in terms of a set of problems — rather than a set of tools — that pertain to data.
So now, how do I get there?
Exploring and venting about quantitative issues
Using large digital libraries to advance literary history
Zoom out, zoom in, zoom out.
Blog to document and reflect on Columbia Data Science Class
A Quick-R Companion
[R] + applied economics.
Scientific computing, data viz and general geekery, with examples in R and MATLAB.