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Introduction to Machine Learning
for Genetics and Genomics Short Course

About the Course

In this introductory course, students will learn important basic concepts in machine learning with a series of hands-on training exercises using R and RStudio. We will explore and discuss different machine learning training strategies and learn on the most important algorithms used in this field such as Random Forests and Support Vector Machines. The capabilities of R caret package will be extensively used and applications in genetics and genomics will be performed with data from public databases. At the end of the course, students will be able to implement a machine learning strategy and to critically evaluate an algorithm’s performance in classification and regression problems. For a detailed course outline, click here.

TARGET AUDIENCE: Graduate students, clinical scholars, bioinformaticians, biologists and health science researchers with a strong interest in statistical methods

PREREQUISITES: Introductory to intermediate programming proficiency in R and R Studio; Basic foundation in statistical modelling (e.g. linear regression).

Course Information

WHEN: Friday, March 30, 2018 from 9:00 am to 5:00 pm

Mount Sinai Annenberg Building
12th floor
Room 12-01
1468 Madison Avenue
New York, NY

REGISTRATION: To register for the course and pay the $375 tuition fee, please click here.

About the Instructor

Joel Correa da Rosa portrait

Dr. Joel Correa da Rosa 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). He worked as an Associate Professor at Federal University of Paraná and Federal Fluminense University. From 2012 to 2015, Dr. Correa da Rosa worked as a Research Associate in Center of Clinical and Translational Science and Laboratory of Investigative Dermatology at the Rockefeller University, becoming Director of Biostatistics in 2015. In 2017 he joined the Population Health Science & Policy Department at the Icahn School of Medicine at Mount Sinai. His expertise includes data analysis, statistical programming, multivariate analysis, and machine learning methods for classification, regression and clustering. He is proficient in R programming and very familiar with SAS, S-Plus, SPSS and MATLAB.