Statistical Modeling in Machine Learning

Concepts and Applications

1st Edition - October 29, 2022
Imprint: Academic Press
Editors: Tilottama Goswami, G. R. Sinha
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 1 7 7 6 - 6
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 7 2 5 2 - 9

Statistical Modeling in Machine Learning: Concepts and Applications presents the basic concepts and roles of statistics, exploratory data analysis and machine learning. The vario… Read more

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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.

Cover image
Title page
Table of Contents
Copyright
Dedication
Contributors
Editors' biographies
Preface
Acknowledgments
1. Introduction to statistical modeling in machine learning: a case study
1.1. Introduction
1.2. Classification of algorithms in machine learning
1.3. Regression algorithms in machine learning
1.4. Case study: prison crowding prediction
1.5. Result and discussion
1.6. Conclusion
2. A technique of data collection: web scraping with python
2.1. Introduction
2.2. Basics of web scraping
2.3. Elements of web scraping
2.4. An implementation walkthrough
2.5. Web scraping in reality
2.6. Conclusion
3. Analysis of Covid-19 using machine learning techniques
3.1. Introduction
3.2. Literature survey
3.3. Study of algorithms
3.4. Experimental analysis and results
3.5. Conclusion and future study
4. Discriminative dictionary learning based on statistical methods
4.1. Introduction
4.2. Notation
4.3. Sparse coding methods
4.4. Dictionary learning
4.5. Statistical concepts in dictionary learning
4.6. Parametric approaches to estimation of dictionary parameters
4.7. Nonparametric approaches to discriminative DL
4.8. Conclusion
5. Artificial intelligence–based uncertainty quantification technique for external flow computational fluid dynamic (CFD) simulations
5.1. Introduction
5.2. Formulation
5.3. Results and discussions
5.4. Conclusions
6. Contrast between simple and complex classification algorithms
6.1. Introduction
6.2. Data preprocessing and feature extraction
6.3. Data modeling
6.4. Conclusion
7. Classification model of machine learning for medical data analysis
7.1. Introduction
7.2. Machine learning techniques for diseases detection
7.3. Disease detected by machine learning techniques
7.4. Challenges in ML based classification for medical data
7.5. Conclusion
8. Regression tasks for machine learning
8.1. Introduction
8.2. Steps in statistical modeling
8.3. General linear regression model
8.4. Simple linear regression (SLR)
8.5. Authentication of the simple linear regression model
8.6. Multiple linear regression
8.7. Polynomial regression
8.8. Implementation using R programming
8.9. Conclusion
9. Model selection and regularization
9.1. Introduction
9.2. Subset selection
9.3. Regularization
9.4. Shrinkage methods
9.5. Dimensional reduction
9.6. Implementation of Ridge and Lasso Regression
9.7. Conclusion
10. Data clustering using unsupervised machine learning
10.1. Introduction
10.2. Techniques in unsupervised learning
10.3. Unsupervised clustering
10.4. Taxonomy of neural network-based deep clustering
10.5. Cluster evolution criteria
10.6. Applications of clustering
10.7. Feature selection with ML for clustering
10.8. Classification in ML: challenges and research issues
10.9. Key findings and open challenges
10.10. Conclusion
11. Emotion-based classification through fuzzy entropy-enhanced FCM clustering
11.1. Introduction
11.2. Related work
11.3. Emotion-based models
11.4. Theoretical background
11.5. Logical design model
11.6. Experimental results
11.7. Conclusion
12. Fundamental optimization methods for machine learning
12.1. Introduction
12.2. First-order optimization methods
12.3. High-order optimization method
12.4. Derivative-free optimization methods
12.5. Optimization methods challenges and issues in machine learning
12.6. Conclusion
13. Stochastic optimization of industrial grinding operation through data-driven robust optimization
13.1. Introduction
13.2. Optimization under uncertainty
13.3. DDRO: data-driven robust optimization for grinding model
13.4. Results and discussions
13.5. Conclusion
14. Dimensionality reduction using PCAs in feature partitioning framework
14.1. Introduction
14.2. Principal component analysis (PCA)
14.3. PCAs in feature partitioning framework
14.4. Summary
15. Impact of Midday Meal Scheme in primary schools in India using exploratory data analysis and data visualization
15.1. Introduction and background
15.2. Nutrition in primary schools in rural India
15.3. Midday Meal Scheme
15.4. Exploratory data analysis and visualization methodology
15.5. Data visualization insights on impact of MDM
15.6. Conclusion
16. Nonlinear system identification of environmental pollutants using recurrent neural networks and Global Sensitivity Analysis
16.1. Introduction
16.2. Formulation
16.3. Results and discussions
16.4. Conclusions
17. Comparative study of automated deep learning techniques for wind time-series forecasting
17.1. Introduction
17.2. Formulation
17.3. Results
17.4. Conclusions
Index

Tilottama Goswami

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.

Affiliations and expertise

Professor, Department of Information Technology, Vasavi College of Engineering, Hyderabad, India

G. R. Sinha

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.

Affiliations and expertise

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

Life Sciences

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