In this introductory course offered by the Center for Biostatistics at the Icahn School of Medicine at Mount Sinai (ISMMS), participants will learn important basic concepts in machine learning with a series of hands-on training exercises using R and RStudio. Different machine learning training strategies will be explored and participants will learn all the most important algorithms used in the field, such as Random Forests and Support Vector Machines. The capabilities of R caret package will be utilized extensively and applications in genetics and genomics will be performed. At the end of the course, participants will implement a machine learning strategy and critically evaluate an algorithm’s performance in classification and regression problems.
Introduction to Machine Learning for Genetics & Genomics will be taught by Dr. Joel Correa da Rosa who holds a M.Sc. degree in Probability and Statistical Inference from State University of Campinas (Sao Paulo / Brazil) and a PhD in Decision Support Methods from the Pontifical Catholic University (PUC-Rio- Rio de Janeiro/Brasil). Dr. Correa da Rosa joined the faculty of the Population Health Science & Policy Department at the Icahn School of Medicine in 2017. His expertise includes data analysis, statistical programming, multivariate analysis, and machine learning methods for classification, regression and clustering.
PREREQUISITES: Introductory to intermediate programming proficiency in R and RStudio; Basic foundation in statistical modelling (e.g. linear regression).
WHEN: Friday, March 30, 2018 from 9:00 am to 5:00 pm
Mount Sinai Annenberg Building
1468 Madison Avenue
New York, NY
REGISTRATION: To register for the course and pay the $375 tuition fee, go to: http://bit.ly/2BOje7i