Supervised Machine Learning Techniques with R

SUPERVISED MACHINE LEARNING TECHNIQUES WITH R

In this workshop, we'll study supervised machine learning methods using R. We’ll cover both the theoretical foundations and practical implementations of various techniques. These include statistical methods like decision trees, random forests, and naïve Bayes classification, alongside mathematical optimization techniques such as gradient boosting, k-nearest neighbors, support vector machines, and artificial, recurrent, and convolutional networks. If time allows, we'll also explore the fascinating world of natural language processing. Data sets and complete R codes will be provided.

About the Instructor

Dr. Olga Korosteleva, a professor of Statistics at California State University, Long Beach since 2002, holds a bachelor's degree in Mathematics from Wayne State University (1996), a Master's in Statistics from Purdue University (1998), and a Ph.D. in Statistics from Purdue University (2002). She has had an extensive experience teaching a diverse array of Statistics/Mathematics courses at undergraduate and graduate levels, actively engages with the local professional association of statisticians (SoCal Chapter of ASA), and has initiated a program for intellectually gifted high school students to conduct research in math/statistics and publish their findings in a journal she recently established.

Host or Publisher Orange County R Users Group