Category Archives: Computational

More free online CS courses from Stanford

Following the free Stanford online course offerings in the Fall 2011 quarter mentioned in this previous post, there are course offerings again this winter, including: probabilistic graphical models and natural language processing, another installment of machine learning, among other courses (scroll to the bottom of any of the linked courses to peruse more).

TGrep2

In response to Lyn’s query about possible positional effects for distributive phrases, I thought I’d post a short bit on how that information might be found with tools that are readily available. TGrep2 is a utility that allows you to conduct a regexp-like search of corpus that’s parsed in Penn Treebank style, and it’s really useful for asking these sorts of questions. Doug Roland has very helpfully posted some executables of TGrep2. For people using intel-based Macs, this is the probably the simplest way to install the tool on your computer. Download the executable, name it tgrep2, make sure it has the right permissions with chmod (executable), and put it in /usr/local/bin. If it’s installed correctly, you should be able to type tgrep2 from a command prompt and have it display some help.

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LSA online Courses access

Some of the LSA Summer institute courses have been added to the following worksheet – courtesy of one of the attending students:

https://docs.google.com/spreadsheet/ccc?key=0AsAWzrlsDdxTdGZUSU5oejRFWC1Ea05xVGdhNnpMMFE&hl=en_US#gid=0

Note: Not all of them are public – some are accessible only if you have a CU account.

Stanford machine learning course

For those of you interested in learning more about machine learning, here’s an interesting opportunity. Andrew Ng at Stanford is offering his annual machine learning class in an online, open access format: Machine Learning. Interested folks will be able to sign up online, watch video lectures and notes, and get feedback on their progress. The class runs October 10th to December 16th, and will touch on most of machine learning’s greatest hits. For people who are interested in getting a firmer grip on the basics of machine learning for their own research, it’d definitely be a worthwhile effort.