Final Workshop (Fall 2022)
Each project must be proposed and developed by teams of 3 students (or less). Exceptions may be granted with written permission from instructor.
Progress Report: (by Nov 16th)
- A PDF document including the following information: Project Title, Team Members, Problem Definition, Description of Data, Description of Methods, and Preliminary Results (optional)
Final report: (by Dec 20th)
- A PDF document including the following information: Project Title, Team Members, Introduction, Problem Definition, Data, Methods, Experiments, Conclusion
Final presentation: (by Dec 20th)
- Students will prepare a presentation, up to 10 minutes, to be delivered in-person during the Final Workshop.
GitHub Repo: (by Dec 20th)
For each of the three deliverables above, a PDF copy must be uploaded to Gradescope before the due dates.
Outstanding projects will get a final grade of
A in this class regardlesss of other scores.
- Tyler 55
- 2:00pm Presentations
- 3:00pm Food and Refreshments
- 3:30pm Presentations
- List of Projects:
- Machine Learning In Modern Speech Recognition -- Adrian, Daniel, Omar
- A Study on an Asset Pricing Model using Machine Learning Techniques -- Nafise, Soode
- RNN Convolutional Code Decoder -- Calvin, Tuyetlinh, Justin (OUTSTANDING project)
- Analysis and Prediction of Uber Fares -- Mark, John, Peyton
- Predicting Voting Propensity and Political Party Affiliation in Primary Elections -- Rodrigo, Zach (OUTSTANDING project)
- Dry Bean Classification System -- Morgan, Melody, Abigail
- Image Upscaling using Machine Learning -- Bennett
- Implementing a Computer Vision Model Using Tensorflow and OpenCV on a Raspberry Pi -- Islam, Joseph, Tommy
- Emoji Prediction Using Bidirectional LSTM -- Arup, Piriyankan (OUTSTANDING project)
- Art Genre Classification -- Borano, Nicholas, Carl
- Music Recommender System -- Mario, Alexander
- Malignant Mole Detection -- Darius, Aiden
- Card Counting with Neural Networks -- Matt, Nikhil
- Medical Procedural Code Embedings for use in Cluster Analysis and Diagnosis Prediction -- Mark