Using Gradient Boosting Machines in Python

Gradient boosting machines (GBM) and related tree-based algorithms have been proved to be highly effective and accurate in a wide range of machine learning tasks. It is also one of the most popular algorithms among participants in Kaggle competitions. This talk will introduce the basic concepts of gradient boosting machines, the commonly used libraries in Python such as XGBoost and LightGBM for performing machine learning using GBM, and ways to tune the hyper-parameters of a model to avoid over/under-fitting and achieve better results. The audience are expected to have basic understanding of machine learning. Codes and slides will be provided on Github after the talk.

Day 1 (4th November) 11:10 AM - 11:40 AM
AC1 Lecture Hall LT-16
English (English Slides)
Basic understanding of machine learning


Photo of Albert Au Yeung

Albert Au Yeung Albert Yu Yeung

Albert is currently a machine learning lead engineer at Zwoop. He was the co-founder and CTO of Axon Labs Limited. He was involved in various projects in data mining and machine learning while being a research at Huawei’s Noah's Ark Lab, the Hong Kong Applied Science and Technology Research Institute (ASTRI), and NTT Communication Science Laboratories in Kyoto, Japan. He has a PhD in Computer Science from the University of Southampton in the U.K. Albert has been using Python since 2004.

Nationality: Hong Kong