Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support

Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support
Author: Kenji Suzuki,Mauricio Reyes,Tanveer Syeda-Mahmood,Ender Konukoglu,Ben Glocker,Roland Wiest,Yaniv Gur,Hayit Greenspan,Anant Madabhushi
Publsiher: Springer Nature
Total Pages: 93
Release: 2019-10-24
ISBN: 3030338509
Category: Computers
Language: EN, FR, DE, ES & NL

Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support Book Excerpt:

This book constitutes the refereed joint proceedings of the Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2019, and the 9th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 7 full papers presented at iMIMIC 2019 and the 3 full papers presented at ML-CDS 2019 were carefully reviewed and selected from 10 submissions to iMIMIC and numerous submissions to ML-CDS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning.

Machine Learning Systems for Multimodal Affect Recognition

Machine Learning Systems for Multimodal Affect Recognition
Author: Markus Kächele
Publsiher: Springer Nature
Total Pages: 188
Release: 2019-11-19
ISBN: 3658286741
Category: Computers
Language: EN, FR, DE, ES & NL

Machine Learning Systems for Multimodal Affect Recognition Book Excerpt:

Markus Kächele offers a detailed view on the different steps in the affective computing pipeline, ranging from corpus design and recording over annotation and feature extraction to post-processing, classification of individual modalities and fusion in the context of ensemble classifiers. He focuses on multimodal recognition of discrete and continuous emotional and medical states. As such, specifically the peculiarities that arise during annotation and processing of continuous signals are highlighted. Furthermore, methods are presented that allow personalization of datasets and adaptation of classifiers to new situations and persons.

The Handbook of Multimodal Multisensor Interfaces Volume 2

The Handbook of Multimodal Multisensor Interfaces  Volume 2
Author: Sharon Oviatt,Björn Schuller,Philip Cohen,Daniel Sonntag,Gerasimos Potamianos,Antonio Krüger
Publsiher: Morgan & Claypool
Total Pages: 555
Release: 2018-10-08
ISBN: 1970001690
Category: Computers
Language: EN, FR, DE, ES & NL

The Handbook of Multimodal Multisensor Interfaces Volume 2 Book Excerpt:

The Handbook of Multimodal-Multisensor Interfaces provides the first authoritative resource on what has become the dominant paradigm for new computer interfaces: user input involving new media (speech, multi-touch, hand and body gestures, facial expressions, writing) embedded in multimodal-multisensor interfaces that often include biosignals. This edited collection is written by international experts and pioneers in the field. It provides a textbook, reference, and technology roadmap for professionals working in this and related areas. This second volume of the handbook begins with multimodal signal processing, architectures, and machine learning. It includes recent deep learning approaches for processing multisensorial and multimodal user data and interaction, as well as context-sensitivity. A further highlight is processing of information about users' states and traits, an exciting emerging capability in next-generation user interfaces. These chapters discuss real-time multimodal analysis of emotion and social signals from various modalities, and perception of affective expression by users. Further chapters discuss multimodal processing of cognitive state using behavioral and physiological signals to detect cognitive load, domain expertise, deception, and depression. This collection of chapters provides walk-through examples of system design and processing, information on tools and practical resources for developing and evaluating new systems, and terminology and tutorial support for mastering this rapidly expanding field. In the final section of this volume, experts exchange views on the timely and controversial challenge topic of multimodal deep learning. The discussion focuses on how multimodal-multisensor interfaces are most likely to advance human performance during the next decade.

Multimodal Agents for Ageing and Multicultural Societies

Multimodal Agents for Ageing and Multicultural Societies
Author: Juliana Miehle,Wolfgang Minker,Elisabeth André,Koichiro Yoshino
Publsiher: Springer Nature
Total Pages: 95
Release: 2021-10-09
ISBN: 9811634769
Category: Computers
Language: EN, FR, DE, ES & NL

Multimodal Agents for Ageing and Multicultural Societies Book Excerpt:

This book aims to explore and discuss theories and technologies for the development of socially competent and culture-aware embodied conversational agents for elderly care. To tackle the challenges in ageing societies, this book was written by experts who have a background in assistive technologies for elderly care, culture-aware computing, multimodal dialogue, social robotics and synthetic agents. Chapter 1 presents a vision of an intelligent agent to illustrate the current challenges for the design and development of adaptive systems. Chapter 2 examines how notions of trust and empathy may be applied to human–robot interaction and how it can be used to create the next generation of emphatic agents, which address some of the pressing issues in multicultural ageing societies. Chapter 3 discusses multimodal machine learning as an approach to enable more effective and robust modelling technologies and to develop socially competent and culture-aware embodied conversational agents for elderly care. Chapter 4 explores the challenges associated with real-world field tests and deployments. Chapter 5 gives a short introduction to socio-cognitive language processing that describes the idea of coping with everyday language, irony, sarcasm, humor, paralinguistic information such as the physical and mental state and traits of the dialogue partner, and social aspects. This book grew out of the Shonan Meeting seminar entitled “Multimodal Agents for Ageing and Multicultural Societies” held in 2018 in Japan. Researchers and practitioners will be helped to understand the emerging field and the identification of promising approaches from a variety of disciplines such as human–computer interaction, artificial intelligence, modelling, and learning.

Technical Advancements of Machine Learning in Healthcare

Technical Advancements of Machine Learning in Healthcare
Author: Hrudaya Kumar Tripathy,Sushruta Mishra,Pradeep Kumar Mallick,Amiya Ranjan Panda
Publsiher: Springer Nature
Total Pages: 388
Release: 2021-02-27
ISBN: 9813346981
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Technical Advancements of Machine Learning in Healthcare Book Excerpt:

This book focuses on various advanced technologies which integrate with machine learning to assist one of the most leading industries, healthcare. It presents recent research works based on machine learning approaches supported by medical and information communication technologies with the use of data and image analysis. The book presents insight about techniques which broadly deals in delivery of quality, accurate and affordable healthcare solutions by predictive, proactive and preventative methods. The book also explores the possible use of machine learning in enterprises, such as enhanced medical imaging/diagnostics, understanding medical data, drug discovery and development, robotic surgery and automation, radiation treatments, creating electronic smart records and outbreak prediction.

Multimodal Analytics for Next Generation Big Data Technologies and Applications

Multimodal Analytics for Next Generation Big Data Technologies and Applications
Author: Kah Phooi Seng,Li-minn Ang,Alan Wee-Chung Liew,Junbin Gao
Publsiher: Springer
Total Pages: 391
Release: 2019-07-18
ISBN: 3319975986
Category: Computers
Language: EN, FR, DE, ES & NL

Multimodal Analytics for Next Generation Big Data Technologies and Applications Book Excerpt:

This edited book will serve as a source of reference for technologies and applications for multimodality data analytics in big data environments. After an introduction, the editors organize the book into four main parts on sentiment, affect and emotion analytics for big multimodal data; unsupervised learning strategies for big multimodal data; supervised learning strategies for big multimodal data; and multimodal big data processing and applications. The book will be of value to researchers, professionals and students in engineering and computer science, particularly those engaged with image and speech processing, multimodal information processing, data science, and artificial intelligence.

Multimodal Scene Understanding

Multimodal Scene Understanding
Author: Michael Yang,Bodo Rosenhahn,Vittorio Murino
Publsiher: Academic Press
Total Pages: 422
Release: 2019-07-16
ISBN: 0128173599
Category: Computers
Language: EN, FR, DE, ES & NL

Multimodal Scene Understanding Book Excerpt:

Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms. Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful. Contains state-of-the-art developments on multi-modal computing Shines a focus on algorithms and applications Presents novel deep learning topics on multi-sensor fusion and multi-modal deep learning

Machine Learning for Multimodal Interaction

Machine Learning for Multimodal Interaction
Author: Samy Bengio,Hervé Bourlard
Publsiher: Springer Science & Business Media
Total Pages: 372
Release: 2005-01-31
ISBN: 354024509X
Category: Computers
Language: EN, FR, DE, ES & NL

Machine Learning for Multimodal Interaction Book Excerpt:

This book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Machine Learning for Multimodal Interaction, MLMI 2004, held in Martigny, Switzerland in June 2004. The 30 revised full papers presented were carefully selected during two rounds of reviewing and revision. The papers are organized in topical sections on HCI and applications, structuring and interaction, multimodal processing, speech processing, dialogue management, and vision and emotion.

The Handbook of Multimodal Multisensor Interfaces Volume 3

The Handbook of Multimodal Multisensor Interfaces  Volume 3
Author: Sharon Oviatt,Björn Schuller,Philip Cohen,Daniel Sonntag,Gerasimos Potamianos,Antonio Krüger
Publsiher: Morgan & Claypool
Total Pages: 813
Release: 2019-06-25
ISBN: 1970001739
Category: Computers
Language: EN, FR, DE, ES & NL

The Handbook of Multimodal Multisensor Interfaces Volume 3 Book Excerpt:

The Handbook of Multimodal-Multisensor Interfaces provides the first authoritative resource on what has become the dominant paradigm for new computer interfaces-user input involving new media (speech, multi-touch, hand and body gestures, facial expressions, writing) embedded in multimodal-multisensor interfaces. This three-volume handbook is written by international experts and pioneers in the field. It provides a textbook, reference, and technology roadmap for professionals working in this and related areas. This third volume focuses on state-of-the-art multimodal language and dialogue processing, including semantic integration of modalities. The development of increasingly expressive embodied agents and robots has become an active test bed for coordinating multimodal dialogue input and output, including processing of language and nonverbal communication. In addition, major application areas are featured for commercializing multimodal-multisensor systems, including automotive, robotic, manufacturing, machine translation, banking, communications, and others. These systems rely heavily on software tools, data resources, and international standards to facilitate their development. For insights into the future, emerging multimodal-multisensor technology trends are highlighted in medicine, robotics, interaction with smart spaces, and similar areas. Finally, this volume discusses the societal impact of more widespread adoption of these systems, such as privacy risks and how to mitigate them. The handbook chapters provide a number of walk-through examples of system design and processing, information on practical resources for developing and evaluating new systems, and terminology and tutorial support for mastering this emerging field. In the final section of this volume, experts exchange views on a timely and controversial challenge topic, and how they believe multimodal-multisensor interfaces need to be equipped to most effectively advance human performance during the next decade.

Multimodal Machine Learning for Human Conversational Behavior Analysis

Multimodal Machine Learning for Human Conversational Behavior Analysis
Author: Lingyu Zhang
Publsiher: Unknown
Total Pages: 0
Release: 2021
ISBN: 1928374650XXX
Category: Electronic Book
Language: EN, FR, DE, ES & NL

Multimodal Machine Learning for Human Conversational Behavior Analysis Book Excerpt:

Multiscale Multimodal Medical Imaging

Multiscale Multimodal Medical Imaging
Author: Xiang Li (Computer scientist)
Publsiher: Springer Nature
Total Pages: 139
Release: 2022
ISBN: 3031188144
Category: Diagnostic imaging
Language: EN, FR, DE, ES & NL

Multiscale Multimodal Medical Imaging Book Excerpt:

This book constitutes the refereed proceedings of the Third International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2022, held in conjunction with MICCAI 2022 in singapore, in September 2022. The 12 papers presented were carefully reviewed and selected from 18 submissions. The MMMI workshop aims to advance the state of the art in multi-scale multi-modal medical imaging, including algorithm development, implementation of methodology, and experimental studies. The papers focus on medical image analysis and machine learning, especially on machine learning methods for data fusion and multi-score learning.

Self directed multimodal learning in higher education

Self directed multimodal learning in higher education
Author: Jako Olivier
Publsiher: AOSIS
Total Pages: 470
Release: 2020-12-31
ISBN: 1928523412
Category: Education
Language: EN, FR, DE, ES & NL

Self directed multimodal learning in higher education Book Excerpt:

This book aims to provide an overview of theoretical and practical considerations in terms of self-directed multimodal learning within the university context. Multimodal learning is approached in terms of the levels of multimodality and specifically blended learning and the mixing of modes of delivery (contact and distance education). As such, this publication will provide a unique snapshot of multimodal practices within higher education through a self-directed learning epistemological lens. The book covers issues such as what self-directed multimodal learning entails, mapping of specific publications regarding blended learning, blended learning in mathematics, geography, natural science and computer literacy, comparative experiences in distance education as well as situated and culturally appropriate learning in multimodal contexts. This book provides a unique focus on multimodality in terms of learning and delivery within the context of self-directed learning. Therefore, the publication would not only advance the scholarship of blended and open distance learning in South Africa, but also the contribute to enriching the discourse regarding self-direction. From this book readers will get an impression of the latest trends in literature in terms of multimodal self-directed learning in South Africa as well as unique empirical work being done in this regard.

Multimodal Learning for Clinical Decision Support and Clinical Image Based Procedures

Multimodal Learning for Clinical Decision Support and Clinical Image Based Procedures
Author: Tanveer Syeda-Mahmood,Klaus Drechsler,Hayit Greenspan,Anant Madabhushi,Alexandros Karargyris,Marius George Linguraru,Cristina Oyarzun Laura,Raj Shekhar,Stefan Wesarg,Miguel Ángel González Ballester,Marius Erdt
Publsiher: Springer Nature
Total Pages: 138
Release: 2020-10-03
ISBN: 3030609464
Category: Computers
Language: EN, FR, DE, ES & NL

Multimodal Learning for Clinical Decision Support and Clinical Image Based Procedures Book Excerpt:

This book constitutes the refereed joint proceedings of the 10th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2020, and the 9th International Workshop on Clinical Image-Based Procedures, CLIP 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. The 4 full papers presented at ML-CDS 2020 and the 9 full papers presented at CLIP 2020 were carefully reviewed and selected from numerous submissions to ML-CDS and 10 submissions to CLIP. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning. The CLIP workshops provides a forum for work centered on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.

Introduction to Machine Learning fourth edition

Introduction to Machine Learning  fourth edition
Author: Ethem Alpaydin
Publsiher: MIT Press
Total Pages: 712
Release: 2020-03-24
ISBN: 0262358069
Category: Computers
Language: EN, FR, DE, ES & NL

Introduction to Machine Learning fourth edition Book Excerpt:

A substantially revised fourth edition of a comprehensive textbook, including new coverage of recent advances in deep learning and neural networks. The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Machine learning underlies such exciting new technologies as self-driving cars, speech recognition, and translation applications. This substantially revised fourth edition of a comprehensive, widely used machine learning textbook offers new coverage of recent advances in the field in both theory and practice, including developments in deep learning and neural networks. The book covers a broad array of topics not usually included in introductory machine learning texts, including supervised learning, Bayesian decision theory, parametric methods, semiparametric methods, nonparametric methods, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing. The fourth edition offers a new chapter on deep learning that discusses training, regularizing, and structuring deep neural networks such as convolutional and generative adversarial networks; new material in the chapter on reinforcement learning that covers the use of deep networks, the policy gradient methods, and deep reinforcement learning; new material in the chapter on multilayer perceptrons on autoencoders and the word2vec network; and discussion of a popular method of dimensionality reduction, t-SNE. New appendixes offer background material on linear algebra and optimization. End-of-chapter exercises help readers to apply concepts learned. Introduction to Machine Learning can be used in courses for advanced undergraduate and graduate students and as a reference for professionals.

Machine Learning for Multimodal Interaction

Machine Learning for Multimodal Interaction
Author: Andrei Popescu-Belis,Steve Renals,Hervé Bourlard
Publsiher: Springer Science & Business Media
Total Pages: 318
Release: 2008-02-26
ISBN: 3540781544
Category: Computers
Language: EN, FR, DE, ES & NL

Machine Learning for Multimodal Interaction Book Excerpt:

This book constitutes the thoroughly refereed post-proceedings of the 4th International Workshop on Machine Learning for Multimodal Interaction, MLMI 2007, held in Brno, Czech Republic, in June 2007. The 25 revised full papers presented together with 1 invited paper were carefully selected during two rounds of reviewing and revision from 60 workshop presentations. The papers are organized in topical sections on multimodal processing, HCI, user studies and applications, image and video processing, discourse and dialogue processing, speech and audio processing, as well as the PASCAL speech separation challenge.

Challenges and Trends in Multimodal Fall Detection for Healthcare

Challenges and Trends in Multimodal Fall Detection for Healthcare
Author: Hiram Ponce,Lourdes Martínez-Villaseñor,Jorge Brieva,Ernesto Moya-Albor
Publsiher: Springer Nature
Total Pages: 259
Release: 2020-01-28
ISBN: 3030387488
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Challenges and Trends in Multimodal Fall Detection for Healthcare Book Excerpt:

This book focuses on novel implementations of sensor technologies, artificial intelligence, machine learning, computer vision and statistics for automated, human fall recognition systems and related topics using data fusion. It includes theory and coding implementations to help readers quickly grasp the concepts and to highlight the applicability of this technology. For convenience, it is divided into two parts. The first part reviews the state of the art in human fall and activity recognition systems, while the second part describes a public dataset especially curated for multimodal fall detection. It also gathers contributions demonstrating the use of this dataset and showing examples. This book is useful for anyone who is interested in fall detection systems, as well as for those interested in solving challenging, signal recognition, vision and machine learning problems. Potential applications include health care, robotics, sports, human–machine interaction, among others.

Artificial Neural Networks and Machine Learning ICANN 2018

Artificial Neural Networks and Machine Learning     ICANN 2018
Author: Věra Kůrková,Yannis Manolopoulos,Barbara Hammer,Lazaros Iliadis,Ilias Maglogiannis
Publsiher: Springer
Total Pages: 632
Release: 2018-09-25
ISBN: 3030014215
Category: Computers
Language: EN, FR, DE, ES & NL

Artificial Neural Networks and Machine Learning ICANN 2018 Book Excerpt:

This three-volume set LNCS 11139-11141 constitutes the refereed proceedings of the 27th International Conference on Artificial Neural Networks, ICANN 2018, held in Rhodes, Greece, in October 2018. The 139 full and 28 short papers as well as 41 full poster papers and 41 short poster papers presented in these volumes was carefully reviewed and selected from total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic Complex Models, Clustering, Mining, Exploratory Analysis, Coding Architectures, Complex Firing Patterns, Convolutional Neural Networks, Deep Learning (DL), DL in Real Time Systems, DL and Big Data Analytics, DL and Big Data, DL and Forensics, DL and Cybersecurity, DL and Social Networks, Evolving Systems – Optimization, Extreme Learning Machines, From Neurons to Neuromorphism, From Sensation to Perception, From Single Neurons to Networks, Fuzzy Modeling, Hierarchical ANN, Inference and Recognition, Information and Optimization, Interacting with The Brain, Machine Learning (ML), ML for Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and Cybersecurity, ML and Social Media, ML in Engineering, Movement and Motion Detection, Multilayer Perceptrons and Kernel Networks, Natural Language, Object and Face Recognition, Recurrent Neural Networks and Reservoir Computing, Reinforcement Learning, Reservoir Computing, Self-Organizing Maps, Spiking Dynamics/Spiking ANN, Support Vector Machines, Swarm Intelligence and Decision-Making, Text Mining, Theoretical Neural Computation, Time Series and Forecasting, Training and Learning.

Optimization in Machine Learning and Applications

Optimization in Machine Learning and Applications
Author: Anand J. Kulkarni,Suresh Chandra Satapathy
Publsiher: Springer Nature
Total Pages: 197
Release: 2019-11-29
ISBN: 9811509948
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Optimization in Machine Learning and Applications Book Excerpt:

This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also presents formulations of real-world machine learning problems, and discusses AI solution methodologies as standalone or hybrid approaches. Lastly, it proposes novel metaheuristic methods to solve complex machine learning problems. Featuring valuable insights, the book helps readers explore new avenues leading toward multidisciplinary research discussions.

Big Data in Multimodal Medical Imaging

Big Data in Multimodal Medical Imaging
Author: Ayman El-Baz,Jasjit S. Suri
Publsiher: CRC Press
Total Pages: 330
Release: 2019-11-05
ISBN: 1351380737
Category: Computers
Language: EN, FR, DE, ES & NL

Big Data in Multimodal Medical Imaging Book Excerpt:

There is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients. The main focus of this book is to review and summarize state-of-the-art big data and deep learning approaches to analyze and integrate multiple data types for the creation of a decision matrix to aid clinicians in the early diagnosis and identification of high risk patients for human diseases and disorders. Leading researchers will contribute original research book chapters analyzing efforts to solve these important problems.

Feature Engineering for Machine Learning and Data Analytics

Feature Engineering for Machine Learning and Data Analytics
Author: Guozhu Dong,Huan Liu
Publsiher: CRC Press
Total Pages: 389
Release: 2018-03-14
ISBN: 1351721267
Category: Business & Economics
Language: EN, FR, DE, ES & NL

Feature Engineering for Machine Learning and Data Analytics Book Excerpt:

Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features. The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively. This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.