Week 2

Today, we transitioned to a new and improved classroom in the Halıcıoğlu Data Science Institute (HDSI). The seats and tables were much better, and there was more open space for us. 

One of our instructors, Professor Soohyun Nam Liao, lectured us on the fundamentals of decision trees, clustering, and supervised vs. unsupervised learning. We learned about machine learning algorithms and the various types of datasets involved. We also studied k-means clustering and related algorithms. 

After lunch, we started working on our labs for the week. Our labs required us to examine data types, split datasets into training and testing sets, train models to make predictions, and calculate the accuracy of the models. 

Today, we continued to work on the ethics videos we started last week for a couple of hours in the morning. 

In the afternoon, we continued working on the lab about decision trees and clustering from the previous day, but if you had already finished with all the labs you were able to go out and continue filming or editing your group's ethics video. 

Today, we were back in the HDSI building, and we started learning about K-Nearest Neighbors and Logistic Regression by one of our amazing instructors, Professor Soohyun Nam Liao. 

Afterwards, we had Professor Benjamin Smarr come into our class and give a lecture about his interesting research on health and AI. 

After lunch, we went to our lab room in the CSE building and started working on our lab for the day. We used the census dataset and applied logistic regression and K-Nearest Neighbors to the dataset. 

Today, we finished working on our Ethics video projects from 9 am to 11 am. Some of us were outside, recording our videos, while others were either making the final edits to the videos or getting ready to submit them. 

Afterwards, we watched all the videos we spent quite a while researching and recording. 

After lunch, we went to our lab room and either finished yesterday's lab or started working on our presentation. The presentation we are creating with a partner is about any lab we completed this past week, and how we went about solving the problem in the lab.