machine learning

Content:

In the class on Monday, we learned some general concepts of machine learning. Machine learning considers the important questions of how to make machines be able to “learn”. I think it is just like teaching a little child to learn. The instructor focused on two of the machine learning tasks-classification, clustering and classification algorithms.


Last week, my roommate asked me a question about endoplasmic reticulum: what organs of the body are abundant in ER? I remember that ER is related to production of protein, and only endocrine system produces protein which composes hormone. We can compare the process of solving the ER problem with the machine learning. The training set records for machine are the biology knowledge I learned in high school and first year of my college; finding the model for classification is the way I remember the knowledge; the result that I make sure it is endocrine system reach the goal that previously unseen records should be assigned a class as accurately as possible.

For the nearest neighbor classifiers, Dr. Ji gave an example of duck which is easy to be understood. As to the k-nearest neighbor model, in my opinion it is based on an assumption that like attracts like. The way to determine the unknown record is the distance to its nearest neighbor, so it is classified according to its neighborhood.

Decision tree is more logical to me. The method to determine the unknown record depends on other attributes. So it is the characteristics that label the class. And the main technique for decision tree is that how to build a tree with attributes.

Support vector machine algorithm has more mathematics. The simple way to explain it is that to find a decision boundary to separate different data. It is not so hard for us to do so because we have intelligent brain, but for machine you have to teach it everything.

Dr. Ji just gave google as an example for clustering. But it is enough to understand that cluster is found by similarity measure among the given data which have a set of attributes.


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Xiaoxiao