Machine Learning With Big Data

The rapid development of digital technologies and advances in communications have led to gigantic amounts of data with complex structures called 'Big data' being produced every day at exponential growth. The aim of this course is to give the student insights in fundamental concepts of machine learning with big data as well as recent research trends in the domain. The student will learn about problems and industrial challenges through domain-based case studies. Furthermore, the student will learn to use tools to develop systems using machine-learning algorithms in big data. About this course Module 1 - Introduction and background Introduction is intended to review Machine learning (ML) and Big Data processing techniques and its related subtopics with the focus on the underlying themes. Module 2 - Case studies Presents case studies from different application domains and discuss key technical issues e.g., noise handling, feature extraction, selection, and learning algorithms in developing such systems. Module 3 - Machine learning techniques in big data analytics This module consists of basic understanding of learning theory, clustering analysis, deep learning and other classification techniques appropriate for development work and issues in construction of systems using Big data. Module 4 - Data analytics with tools Presents open source tools e.g., KNIME and Spark with examples that guide through the basic analysis of big data.bine work and studies. Related industrial challenges addressed in the course * Structure and evaluate the vast amount of data to make sure that it is feasible to solve the customer problem. * Acquire new, previously unknown, knowledge from routinely available huge amount of industrial data to support effective automation, decision-making etc. in industries. * Transform knowledge acquired from the data into machines. This knowledge can be used by automated systems in various fields and provide economic values.