Topics

• Fancy genetics and simple scripts: Manipulating DNA data and becoming more proficient with Python
  By: Mark Mandel
  Length: 20 Minutes
  Description: Our ability to read the genetic code of organisms and to use DNA sequencing to learn new biology has benefited tremendously from technological advances in the past ten years. My lab looks at how animals get colonized with specific bacteria. As we have been generating more data it has become clear that we are underutilizing the information. We are beginning to build resources to be more efficient and clever at data processing and data mining from biological samples. I'll talk a little about the science in the lab and show one of our Python projects that is functional but in its early stages. I am eager for feedback, and I think the talk will have resonance for a new motivated Python user in any field.
• Factor analysis: simplifying high dimensional data sets for visualization and machine learning
  By: Mark Albert
  Length: 25 Minutes
  Description: For many machine learning problems, there are far more dimensions to our data than there need to be for efficient learning. Often a first step is dimensionality reduction to remove both redundancy and noise. In addition to more efficient automated learning, factor analysis allows us to visualize high dimensional data sets in our standard human-limited 2 or 3 dimensions. For demonstration, we will apply PCA on a set of questions asked of the audience to map everyone onto a 2D "personality" map - allowing us to visualize the underlying personality factors of those present. Beyond fun visualizations, these techniques are the basis of more efficient generalization in many machine learning problems.
• Python-fu in the GIMP
  By: Tanya Schlusser
  Length: 25 Minutes
  Description: GIMP (the GNU Image Manipulation Program) is great all by itself but is even better with Python-fu. This talk demonstrates a little Python-fu to manipulate images in GIMP.