RESEARCH SEMINAR COURSE - Fall 2009

Topics in Machine Learning

Fridays, 11:00 AM, 303 Mudd


  1. Ranking: Learning to rank using gradient descent

  2. Stochastic gradients in Machine Learning: SVM Optimization: Inverse Dependence on Training Set Size

  3. Low rank matrix factorization and matrix competion: Maximal Margin Matrix Factorization

  4. Compressive Sensing Compressive Sampling

  5. Compressive Sensing The restricted isometry property and its implications for compressed sensing

  6. More coming soon ...