Machine Learning

Machine Learning at UofT

The Department of Computer Science at the University of Toronto has several faculty members working in the area of machine learning, neural networks, statistical pattern recognition, probabilistic planning, and adaptive systems. In addition, many faculty members inside and outside the department whose primary research interests are in other areas have specific research projects involving machine learning in some way.

  • U of T Machine Learning group spins out company Deep Genomics

    Checkout http://www.deepgenomics.com/ for more details.

  • Reconstructing the evolutionary history of tumors

    Phylo* is a family of statistical methods that use nonparametric Bayesian tree priors to infer clonal evolution of tumors from whole genome sequencing data. References Amit G. Deshwar, Shankar Vembu, Christina K. Yung, Gun Ho Jang, Lincoln Stein, Quaid Morris. PhyloWGS: Reconstructing subclonal composition and evolution from whole genome sequencing of tumors. Genome Biology 16:35, 2015. http://genomebiology.com/2015/16/1/35/abstract https://github.com/morrislab/phylowgs   […]

  • Toronto Deep Learning Projects

    Check out the exciting deep learning research in our group and the new website for deep learning projects! http://deeplearning.cs.toronto.edu/

  • pqR – a pretty quick version of R

    pqR is a new version of the R interpreter. It is based on R-2.15.0, distributed by the R Core Team, but improves on it in many ways, mostly ways that speed it up, but also by implementing some new features and fixing some bugs.

  • SHRiMP

    SHRiMP is a software package for aligning genomic reads against a target genome. It was primarily developed with the multitudinous short reads of next generation sequencing machines in mind, as well as Applied Biosystem’s colourspace genomic representation.

News

  • U of T Machine Learning group spins out company Deep Genomics

    Aug 5th, 15
    [Read More]
  • 7 papers accepted by ICML 2015

    May 8th, 15
    [Read More]
  • Reconstructing the evolutionary history of tumors

    Feb 19th, 15
    [Read More]