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Computational Personal Genomics: Making Sense of Complete Genomes

Graduate · Historical course

Spring 2016

With the growing availability and lowering costs of genotyping and personal genome sequencing, the focus has shifted from the ability to obtain the sequence to the ability to make sense of the resulting information. This course is aimed at exploring the computational challenges associated with interpreting how sequence differences between individuals lead to phenotypic differences in gene expression, disease predisposition, or response to treatment.

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Spring 2005 · Representation and Modeling for Image Analysis

Topics

  • Introduction, Tutorial on PCA
  • Eigenfaces
  • Active Appearance Models
  • Tutorial on EM
  • Learning Flexible Sprites in Video Layers
  • LLE and other Manifold Learning Methods
  • EM Segmentation
  • Tutorial on Calculus of Variations
  • Mumford-Shah
  • Snakes
  • Geodesic Active Contours
  • Shape Based Curve Evolution
  • Tutorial on Clustering
  • Spectral Clustering
  • Normalized Cuts
  • Graph Cuts for Energy Minimization
  • Belief Propagation
  • SIFT
  • Constellation Model
  • Shape Context
  • Local Descriptors
  • Object Recognition
  • Finite Element Models
  • Non-rigid Registration using B-splines
  • Belief Propagation in Image Analysis
  • Doing Photogrammetry Right

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