This week, I worked on the proposal presentation slide deck for our team’s in class presentation. I worked on clarifying what the use case requirements and technical challenges were going to be for the project and researching different values for our quantitative requirements. Specifically, I looked into the frequency and latency of various machine learning models that worked with detecting objects and being able to classify them and found the specific frequency goals we wanted to achieve for our playing/vision area updates. I also looked more into what hardware and software we would be using to perform the classification and what camera we could use that would be easily integrable with the raspberry pi. This week was primarily focused on clarifying our goals and expectations for the project over the next couple of weeks and creating a reasonable timeline for each of the different tasks of the project. My progress is currently on schedule with the project schedule. As the team member on the ML track, according to the schedule, I should currently be starting to read documents and papers that perform similar tasks to what we are trying to accomplish with object detection of cards placed in the vision area and specific card classification. This week, I will be focusing on that specific task, by searching for different papers that implement various networks and ML models that pertain to the task at hand. I will then compile the models that I think will be best fit for our card classification and be ready to test and compare them next week according to the schedule. By the end of the week, I hope to have at least three different ML models researched and understood to ensure that I am ready to test them.