Introduction to Machine Learning for Digital Library Applications

Abstract

This tutorial begins with an overview of the major branches of machine learning (ML) and then provides more thorough coverage of deep neural networks. It covers key concepts, tools, experimental methods, applications, evaluation measures and associated issues for supervised learning (regression and classification), unsupervised learning (clustering and dimensionality reduction), semi-supervised and active learning (which combine the former approaches), and reinforcement learning. The deep neural network discussion covers convolutional neural networks (CNNs), recurrent neural networks (RNNs), word embeddings and related techniques. The discussion will be grounded on digital library (DL) - related applications and will highlight issues, techniques and tools associated with processing big data.