Stochastic Optimization for Large-scale Machine Learning
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- ISBN 9781032131757
- Weight: 480g
- Dimensions: 178 x 254mm
- Publication Date: 19 Nov 2021
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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Advancements in the technology and availability of data sources have led to the `Big Data' era. Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions, but it also creates a lot of challenges such as slow training and scalability of machine learning models. One of the major challenges in machine learning is to develop efficient and scalable learning algorithms, i.e., optimization techniques to solve large scale learning problems.
Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods.
Key Features:
- Bridges machine learning and Optimisation.
- Bridges theory and practice in machine learning.
- Identifies key research areas and recent research directions to solve large-scale machine learning problems.
- Develops optimisation techniques to improve machine learning algorithms for big data problems.
The book will be a valuable reference to practitioners and researchers as well as students in the field of machine learning.
Dr. Vinod Kumar Chauhan is a Research Associate in Industrial Machine Learning in the Institute for Manufacturing, Department of Engineering at University of Cambridge UK. He has a PhD in Machine Learning from Panjab University Chandigarh India. His research interests are in Machine Learning, Optimization and Network Science. He specializes in solving large-scale optimization problems in Machine Learning, handwriting recognition, flight delay propagation in airlines, robustness and nestedness in complex networks and supply chain design using mathematical programming, genetic algorithms and reinforcement learning.
