Friday, September 25, 2009

Discussions from week 9/21-9/25

Content: The idea of machine learning is to observe or analyze data by the machine/computer and being able to take decision on that. Two types of machine learning have been covered in the 1st class by Dr. Shuiwang Ji - classification and clustering. in case of classification data has been divided in two sets - training set and test set. Training set is used to build the model and then using test set the model has been verified. There are several ways for classification, among them vector machine is the one that i used for my MS thesis. One part of my thesis was to detect a fall of a patient. i used accelerometer to collect regular and irregular movement of the patient and then using manifold learning algorithm, i tried to project them on a vector plane and then drew a decision boundary to classify fall or non-fall.


Now the part of machine learning that was discussed in the class was clustering. as far as i understood from the class, it tries to cluster the data in terms of shortest Euclidian distance between them. Hopefully we will learn about some applications of clustering in the next class which might lucid the idea more.



Dr. Pettitti's lecture will definitely help me to design an experiment or a study. Randomized focus group is one major element to choose in a study. for example, if I want to study the people who go to subway for lunch, then if target people only from ABC building then I might end up with wrong result. I have to take samples from every major building around this area to get the right observation. She also discussed different type of study designs and how it differs geographically. She discussed few case studies which have been very useful to comprehend the idea. Also she explained two major terms in study design- prevalence and incidence.




Posted by Gazi

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