Day 30—8/16/17

Today was the last work day of the internship, since tomorrow we will be making our final presentations.  I worked on labelling data in the morning, and after lunch a few of us practiced our presentation in the auditorium.  I spent the rest of the day practicing and reviewing my presentation and managed to get it to be under 10 minutes.  This internship has been a very enjoyable experience for me and I have learnt a lot about eye tracking.

Day 29—8/15/17

Today instead of having the morning meeting, we went to the auditorium and practiced all of our presentations from 9-12.  I found it very helpful to practice my presentation in the room and with the same computer that I will be using for the actual presentation on Thursday.  I took about 11 minutes for my presentation so I will practice it a few more times to try and get it down to 10 minutes.  After lunch, I spent the rest of the day labelling data.  I think I labelled over 300 separate eye movements.

Day 28—8/14/17

Today I worked on practicing what I would say for my presentation in my head and editing some of my slides.  We also were able to run the reinforcement learning sample code, and were able to find the frame-by-frame images of the code that showed the machine learning output to each action made in the Pong-like game.  I had to leave early today since it was the first day of preseason sports.

Day 27—8/11/17

Today I fixed the code and was able to create layered graphs comparing the filtered data and unfiltered data.  I also was able to take videos of the data labelling software and I added them to my presentation.  Then in the afternoon I was able to see how the PowerPoint I created looked on the projector in the auditorium that we will be presenting in on Thursday.  After this, we looked at a sample of a reinforcement learning code that trained a computer to play a simple Pong-like game.

Here is one of the layered graphs I created:

Day 26—8/10/17

Today I continued to work on my PowerPoint and in the afternoon I presented it at the MVRL meeting.  The rest of the afternoon was spent adding the information/editing my presentation based off the advice I got at the meeting.  Almost all of my presentation is finished, except for the graphs that I want to add.  Since the graphs involve layering the filtered data on top of the unfiltered data, I had to go into the older versions of my code to find the functions that created the graphs of the unfiltered data.  When I tried to create the layered graphs, I kept getting the error when the angular velocity of the unfiltered data was calculated.  I will work on fixing that error tomorrow and hopefully will have a final version of my presentation by the end of the day.

Day 25—8/9/17

Today I worked on my presentation and edited eye tracking gaze videos for it.  I was able to finish a draft of my PowerPoint with mostly all of the content included in it along with images for almost every slide.  I also edited some eye tracking videos so that they can be used to explain what saccades and fixations are.

While working on my presentation, I found a Google Q&A that I think does a good job of summarizing the basics of machine learning (it mainly describes supervised learning) in simple terms.  

Here is a link to that article:

https://www.google.com/intl/en/about/main/machine-learning-qa/

Day 24—8/8/17

In the morning, I took notes on videos about reinforcement learning and the example program that we will work on.  After  lunch I continued to take notes on the videos and then also learnt about the relationship between the period of a function and frequency (frequency = 1/period).  Then I added a mean filter to the angular velocity code. I spent the rest of the day working on my presentation since on Thursday, I will be presenting a draft of my powerpoint at the Multidisciplinary Vision Research Lab (MVRL) meeting.  Also in the afternoon, I had the chance to go and see what Ronny and Henry in the optics/laser based manufacturing lab were doing.  It was very interesting to see what they were doing in their lab.

Here is an image of the angular velocity graph that is cleaned and filtered with the Gaussian, mean, and biological filters: