2015 mid-year status and goals

I have been making incremental improvements to the blog, including:

• slight stylistic CSS change (finally know which CSS to fix)
• fixing jQuery plugin for returning search results properly

When there is enough material I will blog about how to customize different parts of this statically generated blog using Pelican.

Learning schedule

Out of all the things that I wish to learn in grad. school (besides Physics and Cosmology obviously), Bayesian stat. and PGMs are some things that I have wanted to learn about for a long time. I believe a better understanding of probabilistic models / stat. concepts will aid my data analysis tremendously. Therefore, I have stopped giving myself excuses and started working on the Coursera material on Probabilistic Graphical Model (PGM).

Related reading that I assigned myself include:

which is the course textbook.

which is one of my favorite textbooks with really concrete examples and accompanying code BRMLToolKit which is written in Matlab. As programming exercises, I have been trying to rewrite some of the code in Python here.

I am curious to see how good the Python package libpgm is which is used in this book. Now I don't know how good the statistics / probability part of the book is but I am willing to give the python package a try without reinventing everything from scratch if it is good.

Finally, since most of the books above contain mostly machine learning / probability concepts, I am adding one last book to the list to gain more perspective from the statistics side of things. It is written by one of my favorite (Bayesian) statisticians Andrew Gelman.

There are other things that I wish to do later in grad. school such as actually practise doing data analysis with Kaggle competitions but I should focus! I am giving myself half a year's time from now just to learn as much about PGM / Bayesian stat. related stuff as possible before diving into Kaggle.