Statistical Modeling in Machine Learning

Statistical Modeling in Machine Learning

Concepts and Applications

1st Edition - November 1, 2022

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  • Editors: Tilottama Goswami, G. R. Sinha
  • Paperback ISBN: 9780323917766

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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.

Key Features

  • 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


Researchers, developers, and industry professionals in Information Technology and Computer Science, such as developers of AI, Machine Learning,and Deep Learning, as well as other research fields, including Biomedical

Table of Contents

  • SECTION A: Introduction and Theoretical Background
    1. Introduction to Statistics
    2. Statistics in Data Preparation and Pre-processing
    3. Statistics for Model Evaluation
    4. Classification Problems inMachine Learning
    5. RegressionTasks inMachine Learning
    6. Unsupervised MachineLearningand ClusteringTasks
    7. Model Selection
    8. Statistics Exploratory Data Analysis for Prediction
    9. Time Series Forecasting with Machine Learning 
    10. Multivariate Real-time Prediction with Machine Learning

    11. Prediction in Social Science Domain
    12. Predictionin HealthEconomicsDomain
    13. Prediction in Finance Domain
    14. Case Study: Prediction in Environmental Science Domain
    15. Case Study: Prediction in Education Domain
    16. Case Study: Prediction in Medical Domain
    17. Conclusion and Future Work

Product details

  • No. of pages: 400
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: November 1, 2022
  • Imprint: Academic Press
  • Paperback ISBN: 9780323917766

About the Editors

Tilottama Goswami

Dr Tilottama Goswami is Professor in Department of Artificial Intelligence, Anurag University, Hyderabad. Her research interest areas are Computer Vision, Machine Learning and Image Processing.

Affiliations and Expertise

Department of Artificial Intelligence, Anurag University, Hyderabad, India

G. R. Sinha

Dr. G R Sinha is Adjunct Professor at the International Institute of Information Technology Bangalore (IIITB), India. His research interests include Medical/Biomedical Image Processing and Cognitive Science applications.

Affiliations and Expertise

Adjunct Professor, International Institute of Information Technology Bangalore (IIITB), India

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