From CS 294-10 Visualization Sp10

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Group Members

Lita Cho & Priyanka Reddy


Graphical models are a formalism underlying much of machine learning, elegantly combining statistics and graph theory to describe and implement probabilistic machine learning approaches. Still, there remains a disconnect between diagrams of graphical models, typically consisting of simple, labeled node-link diagrams with nodes representing random variables over a probability distribution, and the actual statistical mechanics of the system.

We plan on addressing this problem by creating simple tools to visualize certain machine learning algorithms and graphical models. One direction we were thinking about going is automating the creation of Markov models by visualizing the probability distributions associated with nodes, and visualizing the processes of learning and inference. Another direction we could go is creating an automatically generating visualizations of certain machine learning algorithms, like Naive Bayes or perceptron.

Initial Problem Presentation


Midpoint Design Discussion

  • Link to slides here

Final Deliverables

  • Link to source code and executable
  • Link to final paper in pdf form
  • Link to final slides or poster

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