Statistical Modeling in Machine Learning: Concepts and Applications

  • 6h 36m
  • G. R. Sinha, Tilottama Goswami
  • Elsevier Science and Technology Books, Inc.
  • 2022

Statistical Modeling in Machine Learning: Concepts and Applications presents the basic concepts and roles of statistics, exploratory data analysis and machine learning. The various aspects of Machine Learning are discussed along with basics of statistics. Concepts are presented with simple examples and graphical representation for better understanding of techniques. This book takes a holistic approach – putting key concepts together with an in-depth treatise on multi-disciplinary applications of machine learning. New case studies and research problem statements are discussed, which will help researchers in their application areas based on the concepts of statistics and machine learning.

Statistical Modeling in Machine Learning: Concepts and Applications will help statisticians, machine learning practitioners and programmers solving various tasks such as classification, regression, clustering, forecasting, recommending and more.

  • Provides a comprehensive overview of the state-of-the-art in statistical concepts applied to Machine Learning with the help of real-life problems, applications and tutorials
  • Presents a step-by-step approach from fundamentals to advanced techniques
  • Includes Case Studies with both successful and unsuccessful applications of Machine Learning to understand challenges in its implementation, along with worked examples

About the Author

Tilottama Goswami has received a BE degree with Honors in Computer Science and Engineering from the National Institute of Technology, Durgapur; and an MS degree in Computer Science (High Distinction) from Rivier University, Nashua, New Hampshire, United States. She was awarded a PhD in Computer Science from the University of Hyderabad. Presently, Dr. Goswami is Professor in the Department of Information Technology, Vasavi College of Engineering, Hyderabad, India. She has, overall, 23 years of experience in academia, research, and the IT industry. Her research interests are computer vision, machine learning, and image processing. She has been granted an Australian patent for her research work. Dr. Goswami has been conferred with the Distinguished Scientist Award by IJIEMR-Elsevier SSRN, Vijayawada, India. She is also a recipient of the Women Researcher Award, awarded by the REST Society for Research International, India. Dr. Goswami is the recipient of University Grants Commission- Basic Scientific Research (UGC-BSR) Fellowship (under the Government of India). She has been awarded the Star Team Award for developing efficient software for GeoMedia (GIS), leading to complete customer satisfaction at Hexagon (Intergraph), Hyderabad. She is Editorial Board Member of two international journals and has contributed editorial articles and chapters in Scopus-indexed books. Dr. Goswami is an IEEE Senior Member in IEEE CIS/GRSS Chapter Hyderabad Section. She is presently serving as Chairperson of ACM Hyderabad Deccan Professional Chapter and has also served as ACM-W Chair. Dr. Goswami is an active researcher and contributes to society by delivering workshops and guest lectures, participating as technical program committee, tutorial chair, and reviewer in international conferences and journals. She has delivered more than 20 invited talks in the area of artificial intelligence, machine learning algorithms, statistical methods, computer vision, and color image processing. Dr. Goswami has been convener for international events such as Distinguished Lecture Series, AI Webinar Series, Workshops, and Conclaves. Dr. Goswami actively maintains her industry engagement through industry exchange program and project consultancy on applications of AI in various problem domains.

Dr. G R Sinha is a Professor at Myanmar Institute of Information Technology (MIIT) Mandalay, Myanmar. To his credit are 255 research papers, book chapters, and books, including Analysis of Medical Modalities for Improved Diagnosis in Modern Healthcare, Biomedical Signal Processing for Healthcare Applications, Brain and Behavior Computing, and Data Science and Its Applications from Chapman and Hall/CRC Press, Advances in Biometrics from Springer, and Cognitive Informatics, Volumes 1 and 2, AI-Based Brain Computer Interfaces, and Data Deduplication Approaches from Elsevier Academic Press. He was Dean of Faculty and an Executive Council Member of CSVTU and has served as Distinguished Speaker in the field of Digital Image Processing for the Computer Society of India. His research interests include Applications of Machine Learning and Artificial Intelligence in Medical Image Analysis, Biomedical Signal Analysis, Computer Aided Diagnosis, Computer Vision, and Cognitive Science.

In this Book

  • Introduction to statistical modeling in machine learning: A Case Study
  • A Technique of Data Collection: Web Scraping With Python
  • Analysis of Covid-19 using machine learning techniques
  • Discriminative dictionary learning based on statistical methods
  • Artificial intelligence–based uncertainty quantification technique for external flow computational fluid dynamic (CFD) simulations
  • Contrast between simple and complex classification algorithms
  • Classification model of machine learning for medical data analysis
  • Regression tasks for machine learning
  • Model selection and regularization
  • Data clustering using unsupervised machine learning
  • Emotion-based classification through fuzzy entropy-enhanced FCM clustering
  • Fundamental optimization methods for machine learning
  • Stochastic optimization of industrial grinding operation through data-driven robust optimization
  • Dimensionality reduction using PCAs in feature partitioning framework
  • Impact of Midday Meal Scheme in primary schools in India using exploratory data analysis and data visualization
  • Nonlinear system identification of environmental pollutants using recurrent neural networks and Global Sensitivity Analysis
  • Comparative study of automated deep learning techniques for wind time-series forecasting