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Machine Learning 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.

Deliverables

  • 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.

Final Workshop

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This workshop is being sponsored by a TensorFlow and Google AI award to support machine learning courses and diversity programs.

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  • Location:
    • Tyler 55
  • Schedule:
    • 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