Some Data Mining amd Machine Learning Presentations

Statistical Aspects of Data Mining, David Mease

Human Computation, Luis von Anh

Winning the DARPA Grand Challenge, Sebastian Thrun

Scalability and Efficiency on Data Mining Applied to Internet Applocations, Wagner Meira

Sparse and large-scale learning with heterogeneous data, Gert Lanckriet

The first class(ification)-oriented representational formalism, Lev Goldfab

Using Statistics to Search and Annotate Pictures, Nuno Vasconcelos


{Sentence} -> {Word} {Space} {Word} {Stop} -> Hello world!

Welcome to my blog!

I am just about finishing my PhD and that gives me some time to express my opinions and ideas on the science and engineering of intelligent machines. Intelligent machines are machines (or systems of machines) that show abilities we commonly associate with intelligence; these include the capacity to learn, reason, plan and derive and use knowledge from their environment towards achieving some goal.

I am using the term machine in the wide sense, i.e. a machine does not have to be mechanical, it just has to have some machinery (defined as ” A system of related elements that operate in a definable manner”). There we go, you really can’t go far into machine intelligence without considering grammars. Grammars are the rules governing the operations of machines. Grammars say what machines can legally do, how they are defined to operate. Grammars can be viewed from the perspectives of being guides and/or restrictions on the operations of machines. Grammars are everywhere, trust me.

You should have guessed by now that I am a fan of grammars. My doctoral dissertation is on applying Grammatical Evolution to the task of data mining network intrusion data. I have to defend it some time next month, and then I am done (I hope). See you around.