Topic: Clustering K-Means, Learning from Observation

This week we reviewed about supervised, unsupervised and reinforcement learning. After the short review, we got into a part of the main material which is clustering techniques. We learned that clustering techniques is a part of unsupervised learning, and attempts to group and classify data into different segments. It can be applied to almost every known field, some examples include:

• Marketing
• Biology
• Libraries
• City Planning
• etc.

We also learned several distance measurement methods, including the Euclidean distance and the Manhattan Distance. We also learned the two types of clustering techniques, which are Partitional and Hierarchical. Lastly, we learned another one of the main topics which is the K-Means Algorithm, and afterwards did a few exercise questions.