Week Five: Machine Learning & Study Design

Content:

Machine Learning-
At first some of the machine learning lecture just flew over my head but after reviewing the lectures and the comments of colleagues, it looks like the lecture finally arrived. To summarize quickly since there is extensive commenting on Machine Learning in the previous posts, machine learning is a field where we make or give a machine the ability to learn. And this is accomplished through several methods that either classify or cluster. Classification methods includes k-nearest neighbor, decision trees, and support vectors. As Xiaoxiao mentions, Mr. Ji mentioned google as a cluster example and I specifically remember stumbling across an article/webpage that actually describes the algorithms of google as well as talks about how google clusters its data. I will have to review my BME Capstone documentation for the link and I will post it in the comments when I find it.

Study Design-
I found this lecture on study design very interesting since she covered the different types of studies that the different fields conduct (even though the studies may be similar they are called different things in different fields). I especially liked the classic studies that she presented since like she mentioned, these studies are older and are much simpler. They provide a very good basis for understanding the different types of studies. What I liked most was the study on salt (intersalt i believe). It was interesting how the study when it looked at individuals, did not find a correlation between sodium intake and hypertension but when it was performed like it was then the points all lined up and provided a correlation that indicated that salt correlated with blood pressure.

Posted by Eric