CS229: The Netflix Project

CS229 Project: Segmenting Music into Notes using Mixture of Gaussians

Cs229 Project: Factor-analysis with Partially Observed Training Data

Exploring collaborative filters: Neighborhood-based approach

In this project, we study the effectiveness of collaborative filtering mechanisms in the context of the Netflix competition. We focus our attention on a dataset provided by Netflix which includes a training set with more than 100 million 4-tuples: user id, movie id, rating, and date [3]. In the first part of this project, we develop a simple model to predict future ratings of users based on the...

Plead or Pitch? Predicting the Performance of Kickstarter Projects

In this CS229 project, using 26K project proposals from crowdfunding platform Kickstarter, we evaluate the performance of different models (logistic regression, SVMs among others) on predicting whether a project will meet its funding goal or not, with a particular focus on features derived from the language used in project pitches. We then contrast the performance of our best model when using d...

Cs229 Project: Tls, Using Learning to Speculate

We apply machine learning to thread level speculation, a future hardware framework for parallelizing sequential programs. By using machine learning to determine the parallel regions, the overall performance is nearly as good as the best heuristics for each application.