Caltech Stem

The past two weeks have been more towards our field of study than what happened previously. Prior to the second meeting with Dr. Hassibi, the Machine Learning group was given the task to learn about Cluster Algorithms. We spent time studying algorithms such as Expectation-Maximization, k-means and Gaussian Mixture Model. The objective with each subtopic was to understand its purpose, what makes it different from other methods, and the math behind it. This was much more ambiguous than learning what it does. The learning process was somewhat very uncoordinated because situations where you needed to learn an idea to understand a particular thing, leading to more and more topics for us to pick up on. Once all algorithms have been inferred thoroughly, each student was given a topic to write a concept map on. Below is an extensive video going over what Clustering is in Machine Learning: I was assigned to the Expectation-Maximization Algorithm which is an iterative method to

The first four weeks of this Caltech Stem class have been interesting, to say the least. Much of the material we went into have been all over the place with various objectives. We started off with teamwork exercises, watching college students present their research, replicating one for ourselves, writing a concept map, and going to Caltech to meet Dr. Hassibi. We had two, teamwork workshops for the first two weeks. One of which was the dominoes problem where each student was given two dominoes and we were instructed to get three dominoes per person by exchanging dominoes for chips and chips for dominoes. Instructions were very vague other than that everyone must have a domino to "survive" a round. We struggled in the first few attempts but through several days of discussion and trial & error, we were able to find a way to accomplish our goal. Some key underlying goals of this exercise were the importance of leadership, communication, and open-mindedness