Bill.com Datathon Workshop Registration
In preparation for the Rice Datathon 2022 (Presented by Bill.com), Bill.com will be hosting an open workshop on Thursday January 27th for Datathon participants.

Before you register, please read the following guidelines.

1. The workshop is limited to 50 in-person spots. Due to this limit, indication of in-person attendance is BINDING. If you are no longer attending in-person after filling out this form, please contact the Datathon Directors immediately.

2. This workshop is only open to Datathon participants. You must have filled out the Datathon 2022 Registration to be eligible to attend. The Datathon 2022 registration can be accessed here: https://forms.gle/z66NSxH8yjeMEqR46 

This workshop was designed specifically for the Bill.com challenge for Datathon 2022, we hope you are able to take this opportunity to learn from and meet our Presenting Sponsor!

Please reach out to us if you have any additional questions.

- Datathon 2022 Directors

Michael (mpk7@rice.edu)
Gazi (gff1@rice.edu)
Caleb (cdh@rice.edu)
Email *
Workshop: Building a Movie Recommender

Data science courses commonly focus on algorithms that ingest structured data, like tabular data. Although structured data is relatively easy to work with, most of the data collected is not in a predefined format, known as unstructured data. The videos and songs we enjoy, the friends or jobs we connect with, and the items we purchase are all forms of unstructured data. If modeling relationships between entities is important to your data science task, representing the data as a graph can provide powerful insights. In this workshop, we will utilize a graph dataset consisting of Wikipedia links to build a movie recommendation system. We will start with a brief introduction to recommendation systems by utilizing a few simple heuristic based approaches. Next, we will reframe the problem as a graph, and transform the graph in order to calculate similarities between movies. Finally, we will build a deep learning model utilizing the graph data to complete our recommendation system.

Time: January 27th, 7:00-8:30 pm

Location: Duncan Hall 1070 or Virtually on ZOOM

Topics: Jaccard Distance, Vector Spaces, BiGraphs, Word2Vec and PyTorch.

Prerequisites: Python, Linear Algebra, Basic Machine Learning

Presenters from Bill.com: Changlin Zhang, Michael Price, Stefano Romano
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