With all the hype about deep learning and “AI”, it is not well publicized that for structured/tabular data widely encountered in business applications it is actually another machine learning algorithm, the gradient boosting machine (GBM) that most often achieves the highest accuracy. In this workshop I’ll introduce GBMs and several implementations (accessible from R or Python) and we’ll train and tune GBMs on some public datasets using R and Python (hands-on).
Chief Scientist, Epoch USA
Szilard studied Physics in the 90s and obtained a PhD by using statistical methods to analyze the risk of financial portfolios. For the last decade he’s been the Chief Scientist of a tech company in California doing everything data (analysis, modeling, data visualization, data engineering, machine learning etc). He is the founder of the LA R and LA data science meetups and the data community website datascience.la, he is the author of a well-known machine learning benchmark on github (1000+ stars), a frequent speaker at data science conferences, and he has developed and taught graduate machine learning courses at two universities (UCLA and CEU).