Machine Learning in Computational Biology (the frequentist approach)
published: May 13, 2014, recorded: April 2014, views: 3745
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
These lecture will introduce some general concepts and algorithms in statistical learning, illustrating them through applications in biology and personalized medicine. I will discuss linear methods in classification and regression, nonlinear extensions with positive definite kernels, and feature selection and structured sparsity. Application will include molecular diagnosis and prognosis in cancer, virtual screening in drug discovery, and biological network inference.
- Introduction to pattern recognition and regression for biology and personalized medicine
- Linear methods for regression and classification (OLS, RR, LDA, QDA, logistic regression, SVM...)
- Nonlinear extensions with kernels
- Feature selection and structured sparsity (lasso and variants)
- Application: cancer prognosis from genomic data
- Application: drug discovery
- Application: gene networks inference
Download slides: mlpmsummerschool2013_vert_computational_biology.pdf (9.4 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !