Zum Hauptinhalt springen
Dekorationsartikel gehören nicht zum Leistungsumfang.
Data Science and Predictive Analytics
Biomedical and Health Applications using R
Buch von Ivo D. Dinov
Sprache: Englisch

117,69 €*

inkl. MwSt.

Versandkostenfrei per Post / DHL

Aktuell nicht verfügbar

Kategorien:
Beschreibung
This textbook integrates important mathematical foundations, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and decision science challenges. Each concept in the book includes a rigorous symbolic formulation coupled with computational algorithms and complete end-to-end pipeline protocols implemented as functional R electronic markdown notebooks. These workflows support active learning and demonstrate comprehensive data manipulations, interactive visualizations, and sophisticated analytics. The content includes open problems, state-of-the-art scientific knowledge, ethical integration of heterogeneous scientific tools, and procedures for systematic validation and dissemination of reproducible research findings.
Complementary to the enormous challenges related to handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there are unique opportunities that come with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics covered in Data Science and Predictive Analytics address specific knowledge gaps, resolve educational barriers, and mitigate workforce information-readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical principles, modern computational methods, advanced data science techniques, model-based machine learning, model-free artificial intelligence, and innovative biomedical applications. The book¿s fourteen chapters start with an introduction and progressively build foundational skills from visualization to linear modeling, dimensionality reduction, supervised classification, black-box machine learning techniques, qualitative learning methods, unsupervised clustering, model performance assessment, feature selection strategies, longitudinal data analytics, optimization, neural networks, and deep learning. The second edition of the book includes additional learning-based strategies utilizing generative adversarial networks, transfer learning, and synthetic data generation, as well as eight complementary electronic appendices.
This textbook is suitable for formal didactic instructor-guided course education, as well as for individual or team-supported self-learning. The material is presented at the upper-division and graduate-level college courses and covers applied and interdisciplinary mathematics, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical sciences. The analytical techniques and predictive scientific methods described in the book may be useful to a wide range of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory, funding, and policy agencies. The supporting book website provides many examples, datasets, functional scripts, complete electronic notebooks, extensive appendices, and additional materials.
This textbook integrates important mathematical foundations, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and decision science challenges. Each concept in the book includes a rigorous symbolic formulation coupled with computational algorithms and complete end-to-end pipeline protocols implemented as functional R electronic markdown notebooks. These workflows support active learning and demonstrate comprehensive data manipulations, interactive visualizations, and sophisticated analytics. The content includes open problems, state-of-the-art scientific knowledge, ethical integration of heterogeneous scientific tools, and procedures for systematic validation and dissemination of reproducible research findings.
Complementary to the enormous challenges related to handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there are unique opportunities that come with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics covered in Data Science and Predictive Analytics address specific knowledge gaps, resolve educational barriers, and mitigate workforce information-readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical principles, modern computational methods, advanced data science techniques, model-based machine learning, model-free artificial intelligence, and innovative biomedical applications. The book¿s fourteen chapters start with an introduction and progressively build foundational skills from visualization to linear modeling, dimensionality reduction, supervised classification, black-box machine learning techniques, qualitative learning methods, unsupervised clustering, model performance assessment, feature selection strategies, longitudinal data analytics, optimization, neural networks, and deep learning. The second edition of the book includes additional learning-based strategies utilizing generative adversarial networks, transfer learning, and synthetic data generation, as well as eight complementary electronic appendices.
This textbook is suitable for formal didactic instructor-guided course education, as well as for individual or team-supported self-learning. The material is presented at the upper-division and graduate-level college courses and covers applied and interdisciplinary mathematics, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical sciences. The analytical techniques and predictive scientific methods described in the book may be useful to a wide range of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory, funding, and policy agencies. The supporting book website provides many examples, datasets, functional scripts, complete electronic notebooks, extensive appendices, and additional materials.
Über den Autor
Professor Ivo D. Dinov directs the Statistics Online Computational Resource (SOCR) at the University of Michigan and serves as associate director of the Michigan Institute for Data Science (MIDAS). He is an expert in mathematical modeling, statistical analysis, high-throughput computational processing, and scientific visualization of large, complex and heterogeneous datasets (Big Data). Dr. Dinov is developing, validating, and disseminating novel technology-enhanced pedagogical approaches for STEM education and active data science learning. His artificial intelligence and machine learning work involves compressive big data analytics, statistical obfuscation of sensitive data, complex time (kime) representation, model-based and model-free techniques for kimesurface analytics. Dr. Dinov is a member of the American Statistical Association, the American Mathematical Society, the American Physical Society, the American Association for the Advancement of Science, an honorary member ofthe Sigma Theta Tau International Society, and an elected member of the International Statistical Institute.
Zusammenfassung

Transdisciplinary treatment integrates novel computational methods, statistical inference techniques, data science tools

Includes many hands-on demonstrations using imaging, environmental, health and clinical case-studies

Promotes open-source code, data sharing, and open-science principles

Inhaltsverzeichnis
1 Front Matter
Foreword
DSPA Application and Use Disclaimer
2nd Edition Preface
Book Content
Notations
1 Chapter 1 - Introduction
1.1 Motivation
1.1.1 DSPA Mission and Objectives
1.1.2 Examples of driving motivational problems and challenges
1.1.3 Common Characteristics of Big (Biomedical and Health) Data
1.1.4 Data Science
1.1.5 Predictive Analytics
1.1.6 High-throughput Big Data Analytics
1.1.7 Examples of data repositories, archives and services
1.1.8 Responsible Data Science and Ethical Predictive Analytics
1.1.9 DSPA Expectations
1.2 Foundations of R
1.2.1 Why use R?
1.2.2 Getting started with R
1.2.3 Mathematics, Statistics, and Optimization
1.2.4 Advanced Data Processing
1.2.5 Basic Plotting
1.2.6 Basic R Programming
1.2.7 Data Simulation Primer
1.3 Practice Problems
1.3.1 Long-to-Wide Data format translation
1.3.2 Data Frames
1.3.3 Data stratification
1.3.4 Simulation
1.3.5 Programming
1.4 Appendix
1.4.1 Tidyverse
1.4.2 Additional R documentation and resources
1.4.3 HTML SOCR Data Import
1.4.4 R Debugging
2 Chapter 2: Basic Visualization and Exploratory Data Analytics
2.1 Data Handling
2.1.1 Saving and Loading R Data Structures
2.1.2 Importing and Saving Data from CSV Files
2.1.3 Importing Data from ZIP and SAV Files
2.1.4 Exploring the Structure of Data
2.1.5 Exploring Numeric Variables
2.1.6 Measuring Central Tendency - mean, median, mode
2.1.7 Measuring Spread - variance, quartiles and the five-number summary
2.1.8 Visualizing Numeric Variables - boxplots
2.1.9 Visualizing Numeric Variables - histograms
2.1.10 Uniform and normal distributions
2.1.11 Exploring Categorical Variables
2.1.12 Exploring Relationships Between Variables
2.1.13 Missing Data
2.1.14 Parsing web pages and visualizing tabular HTML data
2.1.15 Cohort-Rebalancing (for Imbalanced Groups)
2.2 Exploratory Data Analytics (EDA)
2.2.1 Classification of visualization methods
2.2.2 Composition
2.2.3 Comparison
2.2.4 Relationships
2.3 Practice Problems
2.3.1 Data Manipulation
2.3.2 Bivariate relations
2.3.3 Missing data
2.3.4 Surface plots
2.3.5 Unbalanced groups
2.3.6 Common plots
2.3.7 Trees and Graphs
2.3.8 Data EDA examples
2.3.9 Data reports
3 Chapter 3: Linear Algebra, Matrix Computing and Regression Modeling
3.1 Linear Algebra
3.1.1 Building Matrices
3.1.2 Matrix subscripts
3.1.3 Addition and subtraction
3.1.4 Multiplication
3.2 Matrix Computing
3.2.1 Solving Systems of Equations
3.2.2 The identity matrix
3.2.3 Vectors, Matrices, and Scalars
3.2.4 Sample Statistics
3.2.5 Applications of Matrix Algebra in Linear Modeling
3.2.6 Finding function extrema (min/max) using calculus
3.2.7 Linear modeling in R
3.3 Eigenspectra - Eigenvalues and Eigenvectors
3.4 Matrix notation
3.5 Linear regression
3.5.1 Sample covariance matrix
3.6 Linear multivariate linear regression modeling
3.6.1 Simple linear regression
3.6.2 Ordinary least squares estimation
3.6.3 Regression Model Assumptions
3.6.4 Correlations
3.6.5 Multiple Linear Regression
3.7 Case Study 1: Baseball Players
3.7.1 Step 1 - collecting data
3.7.2 Step 2 - exploring and preparing the data
3.7.3 Step 3 - training a model on the data
3.7.4 Step 4 - evaluating model performance
3.7.5 Step 5 - improving model performance
3.8 Regression trees and model trees
3.8.1 Adding regression to trees
3.9 Bayesian Additive Regression Trees (BART)
3.9.1 1D Simulation
3.9.2 Higher-Dimensional Simulation
3.9.3 Heart Attack Hospitalization Case-Study
3.9.4 Another look at Case study 2: Baseball Players
3.10 Practice Problems
3.10.1 How is matrix multiplication defined?
3.10.2 Scalar vs. Matrix Multiplication
3.10.3 Matrix Equations
3.10.4 Least Square Estimation
3.10.5 Matrix manipulation
3.10.6 Matrix Transposition
3.10.7 Sample Statistics
3.10.8 Eigenvalues and Eigenvectors
3.10.9 Regression Forecasting using Numerical Data
4 Chapter 4: Linear and Nonlinear Dimensionality Reduction
4.1 Motivational Example: Reducing 2D to 1D
4.2 Matrix Rotations
4.3 Summary (PCA, ICA, and FA)
4.4 Principal Component Analysis (PCA)
4.4.1 Principal Components
4.5 Independent component analysis (ICA)
4.6 Factor Analysis (FA)
4.7 Singular Value Decomposition (SVD)
4.7.1 SVD Summary
4.8 t-distributed Stochastic Neighbor Embedding (t-SNE)
4.8.1 t-SNE Formulation
4.8.2 t-SNE Example: Hand-written Digit Recognition
4.9 Uniform Manifold Approximation and Projection (UMAP)
4.9.1 Mathematical formulation
4.9.2 Hand-Written Digits Recognition
4.9.3 Apply UMAP for class-prediction using new data
4.10 UMAP Parameters
4.10.1 Stability, Replicability, and Reproducibility
4.10.2 UMAP Interpretation
4.11 Dimensionality Reduction Case Study (Parkinson's Disease)
4.11.1 Step 1: Collecting Data
4.11.2 Step 2: Exploring and preparing the data
4.11.3 PCA
4.11.4 Factor analysis (FA)
4.11.5 t-SNE
4.11.6 Uniform Manifold Approximation and Projection (UMAP)
4.12 Practice Problems
4.12.1 Parkinson's Disease example
4.12.2 Allometric Relations in Plants example
4.12.3 3D Volumetric Brain Study
5 Chapter 5: Supervised Classification
5.1 k-Nearest Neighbor Approach
5.2 Distance Function and Dummy coding
5.2.1 Estimation of the hyperparameter k
5.2.2 Rescaling of the features
5.2.3 Rescaling Formulas
5.2.4 Case Study: Youth Development
5.2.5 Case Study: Predicting Galaxy Spins
5.3 Probabilistic Learning - Naïve Bayes Classification
5.3.1 Overview of the Naive Bayes Method
5.3.2 Model Assumptions
5.3.3 Bayes Formula
5.3.4 The Laplace Estimator
5.3.5 Case Study: Head and Neck Cancer Medication
5.4 Decision Trees and Divide and Conquer Classification
5.4.1 Motivation
5.4.2 Decision Tree Overview
5.4.3 Case Study 1: Quality of Life and Chronic Disease
5.4.4 Classification rules
5.5 Case Study 2: QoL in Chronic Disease (Take 2)
5.6 Practice Problems
5.6.1 Iris Species
5.6.2 Cancer Study
5.6.3 Baseball Data
5.6.4 Medical Specialty Text-Notes Classification
5.6.5 Chronic Disease Case-Study
6 Chapter 6: Black Box Machine Learning Methods
6.1 Neural Networks
6.1.1 From biological to artificial neurons
6.1.2 Activation functions
6.2 Network topology
6.2.1 Network layers
6.2.2 Training neural networks with backpropagation
6.2.3 Case Study 1: Google Trends and the Stock Market - Regression
6.2.4 Simple NN demo - learning to compute
6.2.5 Case Study 2: Google Trends and the Stock Market - Classification
6.3 Support Vector Machines (SVM)
6.3.1 Classification with hyperplanes
6.3.2 Case Study 3: Optical Character Recognition (OCR)
6.3.3 Case Study 4: Iris Flowers
6.3.4 Parameter Tuning
6.3.5 Improving the performance of Gaussian kernels
6.4 Ensemble meta-learning
6.4.1 Bagging
6.4.2 Boosting
6.4.3 Random forests
6.4.4 Random Forest Algorithm (Pseudo Code)
6.4.5 Adaptive boosting
6.5 Practice Problems
6.5.1 Problem 1: Google Trends and the Stock Market
6.5.2 Problem 2: Quality of Life and Chronic Disease
7 Chapter 7: Qualitative Learning Methods - Text Mining, Natural Language Processing, Apriori Association Rules Learning
7.1 Natural Language Processing (NLP) and Text Mining (TM)
7.1.1 A simple NLP/TM example
7.1.2 Case-Study: Job ranking
7.1.3 Area Under ROC Curve
7.1.4 TF-IDF
7.1.5 Cosine similarity
7.1.6 Sentiment analysis
7.1.7 NLP/TM Analytics
7.2 Apriori Association Rules Learning
7.2.1 Association Rules
7.2.2 The Apriori algorithm for association rule learning
7.2.3 Rule support and confidence
7.2.4 Building a set of rules with the Apriori principle
7.2.5 A toy example
7.2.6 Case Study 1: Head and Neck Cancer Medications
7.2.7 Graphical depiction of association rules
7.2.8 Saving association rules to a file or a data frame
7.3 Summary
7.4 Practice Problems
7.4.1 Groceries
7.4.2 Titanic Passengers
8 Chapter 8: Unsupervised Clustering
8.1 ML Clustering
8.2 Silhouette plots
8.3 The k-Means Clustering Algorithm
8.3.1 Pseudocode
8.3.2 Choosing the appropriate number of clusters
8.3.3 Case Study 1: Divorce and Consequences on Young...
Details
Erscheinungsjahr: 2023
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Reihe: The Springer Series in Applied Machine Learning
Inhalt: xxxiv
918 S.
30 s/w Illustr.
306 farbige Illustr.
918 p. 336 illus.
306 illus. in color.
ISBN-13: 9783031174827
ISBN-10: 3031174828
Sprache: Englisch
Ausstattung / Beilage: HC runder Rücken kaschiert
Einband: Gebunden
Autor: Dinov, Ivo D.
Auflage: 2nd ed. 2023
Hersteller: Springer International Publishing
The Springer Series in Applied Machine Learning
Maße: 241 x 160 x 56 mm
Von/Mit: Ivo D. Dinov
Erscheinungsdatum: 17.02.2023
Gewicht: 1,572 kg
Artikel-ID: 123641519
Über den Autor
Professor Ivo D. Dinov directs the Statistics Online Computational Resource (SOCR) at the University of Michigan and serves as associate director of the Michigan Institute for Data Science (MIDAS). He is an expert in mathematical modeling, statistical analysis, high-throughput computational processing, and scientific visualization of large, complex and heterogeneous datasets (Big Data). Dr. Dinov is developing, validating, and disseminating novel technology-enhanced pedagogical approaches for STEM education and active data science learning. His artificial intelligence and machine learning work involves compressive big data analytics, statistical obfuscation of sensitive data, complex time (kime) representation, model-based and model-free techniques for kimesurface analytics. Dr. Dinov is a member of the American Statistical Association, the American Mathematical Society, the American Physical Society, the American Association for the Advancement of Science, an honorary member ofthe Sigma Theta Tau International Society, and an elected member of the International Statistical Institute.
Zusammenfassung

Transdisciplinary treatment integrates novel computational methods, statistical inference techniques, data science tools

Includes many hands-on demonstrations using imaging, environmental, health and clinical case-studies

Promotes open-source code, data sharing, and open-science principles

Inhaltsverzeichnis
1 Front Matter
Foreword
DSPA Application and Use Disclaimer
2nd Edition Preface
Book Content
Notations
1 Chapter 1 - Introduction
1.1 Motivation
1.1.1 DSPA Mission and Objectives
1.1.2 Examples of driving motivational problems and challenges
1.1.3 Common Characteristics of Big (Biomedical and Health) Data
1.1.4 Data Science
1.1.5 Predictive Analytics
1.1.6 High-throughput Big Data Analytics
1.1.7 Examples of data repositories, archives and services
1.1.8 Responsible Data Science and Ethical Predictive Analytics
1.1.9 DSPA Expectations
1.2 Foundations of R
1.2.1 Why use R?
1.2.2 Getting started with R
1.2.3 Mathematics, Statistics, and Optimization
1.2.4 Advanced Data Processing
1.2.5 Basic Plotting
1.2.6 Basic R Programming
1.2.7 Data Simulation Primer
1.3 Practice Problems
1.3.1 Long-to-Wide Data format translation
1.3.2 Data Frames
1.3.3 Data stratification
1.3.4 Simulation
1.3.5 Programming
1.4 Appendix
1.4.1 Tidyverse
1.4.2 Additional R documentation and resources
1.4.3 HTML SOCR Data Import
1.4.4 R Debugging
2 Chapter 2: Basic Visualization and Exploratory Data Analytics
2.1 Data Handling
2.1.1 Saving and Loading R Data Structures
2.1.2 Importing and Saving Data from CSV Files
2.1.3 Importing Data from ZIP and SAV Files
2.1.4 Exploring the Structure of Data
2.1.5 Exploring Numeric Variables
2.1.6 Measuring Central Tendency - mean, median, mode
2.1.7 Measuring Spread - variance, quartiles and the five-number summary
2.1.8 Visualizing Numeric Variables - boxplots
2.1.9 Visualizing Numeric Variables - histograms
2.1.10 Uniform and normal distributions
2.1.11 Exploring Categorical Variables
2.1.12 Exploring Relationships Between Variables
2.1.13 Missing Data
2.1.14 Parsing web pages and visualizing tabular HTML data
2.1.15 Cohort-Rebalancing (for Imbalanced Groups)
2.2 Exploratory Data Analytics (EDA)
2.2.1 Classification of visualization methods
2.2.2 Composition
2.2.3 Comparison
2.2.4 Relationships
2.3 Practice Problems
2.3.1 Data Manipulation
2.3.2 Bivariate relations
2.3.3 Missing data
2.3.4 Surface plots
2.3.5 Unbalanced groups
2.3.6 Common plots
2.3.7 Trees and Graphs
2.3.8 Data EDA examples
2.3.9 Data reports
3 Chapter 3: Linear Algebra, Matrix Computing and Regression Modeling
3.1 Linear Algebra
3.1.1 Building Matrices
3.1.2 Matrix subscripts
3.1.3 Addition and subtraction
3.1.4 Multiplication
3.2 Matrix Computing
3.2.1 Solving Systems of Equations
3.2.2 The identity matrix
3.2.3 Vectors, Matrices, and Scalars
3.2.4 Sample Statistics
3.2.5 Applications of Matrix Algebra in Linear Modeling
3.2.6 Finding function extrema (min/max) using calculus
3.2.7 Linear modeling in R
3.3 Eigenspectra - Eigenvalues and Eigenvectors
3.4 Matrix notation
3.5 Linear regression
3.5.1 Sample covariance matrix
3.6 Linear multivariate linear regression modeling
3.6.1 Simple linear regression
3.6.2 Ordinary least squares estimation
3.6.3 Regression Model Assumptions
3.6.4 Correlations
3.6.5 Multiple Linear Regression
3.7 Case Study 1: Baseball Players
3.7.1 Step 1 - collecting data
3.7.2 Step 2 - exploring and preparing the data
3.7.3 Step 3 - training a model on the data
3.7.4 Step 4 - evaluating model performance
3.7.5 Step 5 - improving model performance
3.8 Regression trees and model trees
3.8.1 Adding regression to trees
3.9 Bayesian Additive Regression Trees (BART)
3.9.1 1D Simulation
3.9.2 Higher-Dimensional Simulation
3.9.3 Heart Attack Hospitalization Case-Study
3.9.4 Another look at Case study 2: Baseball Players
3.10 Practice Problems
3.10.1 How is matrix multiplication defined?
3.10.2 Scalar vs. Matrix Multiplication
3.10.3 Matrix Equations
3.10.4 Least Square Estimation
3.10.5 Matrix manipulation
3.10.6 Matrix Transposition
3.10.7 Sample Statistics
3.10.8 Eigenvalues and Eigenvectors
3.10.9 Regression Forecasting using Numerical Data
4 Chapter 4: Linear and Nonlinear Dimensionality Reduction
4.1 Motivational Example: Reducing 2D to 1D
4.2 Matrix Rotations
4.3 Summary (PCA, ICA, and FA)
4.4 Principal Component Analysis (PCA)
4.4.1 Principal Components
4.5 Independent component analysis (ICA)
4.6 Factor Analysis (FA)
4.7 Singular Value Decomposition (SVD)
4.7.1 SVD Summary
4.8 t-distributed Stochastic Neighbor Embedding (t-SNE)
4.8.1 t-SNE Formulation
4.8.2 t-SNE Example: Hand-written Digit Recognition
4.9 Uniform Manifold Approximation and Projection (UMAP)
4.9.1 Mathematical formulation
4.9.2 Hand-Written Digits Recognition
4.9.3 Apply UMAP for class-prediction using new data
4.10 UMAP Parameters
4.10.1 Stability, Replicability, and Reproducibility
4.10.2 UMAP Interpretation
4.11 Dimensionality Reduction Case Study (Parkinson's Disease)
4.11.1 Step 1: Collecting Data
4.11.2 Step 2: Exploring and preparing the data
4.11.3 PCA
4.11.4 Factor analysis (FA)
4.11.5 t-SNE
4.11.6 Uniform Manifold Approximation and Projection (UMAP)
4.12 Practice Problems
4.12.1 Parkinson's Disease example
4.12.2 Allometric Relations in Plants example
4.12.3 3D Volumetric Brain Study
5 Chapter 5: Supervised Classification
5.1 k-Nearest Neighbor Approach
5.2 Distance Function and Dummy coding
5.2.1 Estimation of the hyperparameter k
5.2.2 Rescaling of the features
5.2.3 Rescaling Formulas
5.2.4 Case Study: Youth Development
5.2.5 Case Study: Predicting Galaxy Spins
5.3 Probabilistic Learning - Naïve Bayes Classification
5.3.1 Overview of the Naive Bayes Method
5.3.2 Model Assumptions
5.3.3 Bayes Formula
5.3.4 The Laplace Estimator
5.3.5 Case Study: Head and Neck Cancer Medication
5.4 Decision Trees and Divide and Conquer Classification
5.4.1 Motivation
5.4.2 Decision Tree Overview
5.4.3 Case Study 1: Quality of Life and Chronic Disease
5.4.4 Classification rules
5.5 Case Study 2: QoL in Chronic Disease (Take 2)
5.6 Practice Problems
5.6.1 Iris Species
5.6.2 Cancer Study
5.6.3 Baseball Data
5.6.4 Medical Specialty Text-Notes Classification
5.6.5 Chronic Disease Case-Study
6 Chapter 6: Black Box Machine Learning Methods
6.1 Neural Networks
6.1.1 From biological to artificial neurons
6.1.2 Activation functions
6.2 Network topology
6.2.1 Network layers
6.2.2 Training neural networks with backpropagation
6.2.3 Case Study 1: Google Trends and the Stock Market - Regression
6.2.4 Simple NN demo - learning to compute
6.2.5 Case Study 2: Google Trends and the Stock Market - Classification
6.3 Support Vector Machines (SVM)
6.3.1 Classification with hyperplanes
6.3.2 Case Study 3: Optical Character Recognition (OCR)
6.3.3 Case Study 4: Iris Flowers
6.3.4 Parameter Tuning
6.3.5 Improving the performance of Gaussian kernels
6.4 Ensemble meta-learning
6.4.1 Bagging
6.4.2 Boosting
6.4.3 Random forests
6.4.4 Random Forest Algorithm (Pseudo Code)
6.4.5 Adaptive boosting
6.5 Practice Problems
6.5.1 Problem 1: Google Trends and the Stock Market
6.5.2 Problem 2: Quality of Life and Chronic Disease
7 Chapter 7: Qualitative Learning Methods - Text Mining, Natural Language Processing, Apriori Association Rules Learning
7.1 Natural Language Processing (NLP) and Text Mining (TM)
7.1.1 A simple NLP/TM example
7.1.2 Case-Study: Job ranking
7.1.3 Area Under ROC Curve
7.1.4 TF-IDF
7.1.5 Cosine similarity
7.1.6 Sentiment analysis
7.1.7 NLP/TM Analytics
7.2 Apriori Association Rules Learning
7.2.1 Association Rules
7.2.2 The Apriori algorithm for association rule learning
7.2.3 Rule support and confidence
7.2.4 Building a set of rules with the Apriori principle
7.2.5 A toy example
7.2.6 Case Study 1: Head and Neck Cancer Medications
7.2.7 Graphical depiction of association rules
7.2.8 Saving association rules to a file or a data frame
7.3 Summary
7.4 Practice Problems
7.4.1 Groceries
7.4.2 Titanic Passengers
8 Chapter 8: Unsupervised Clustering
8.1 ML Clustering
8.2 Silhouette plots
8.3 The k-Means Clustering Algorithm
8.3.1 Pseudocode
8.3.2 Choosing the appropriate number of clusters
8.3.3 Case Study 1: Divorce and Consequences on Young...
Details
Erscheinungsjahr: 2023
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Reihe: The Springer Series in Applied Machine Learning
Inhalt: xxxiv
918 S.
30 s/w Illustr.
306 farbige Illustr.
918 p. 336 illus.
306 illus. in color.
ISBN-13: 9783031174827
ISBN-10: 3031174828
Sprache: Englisch
Ausstattung / Beilage: HC runder Rücken kaschiert
Einband: Gebunden
Autor: Dinov, Ivo D.
Auflage: 2nd ed. 2023
Hersteller: Springer International Publishing
The Springer Series in Applied Machine Learning
Maße: 241 x 160 x 56 mm
Von/Mit: Ivo D. Dinov
Erscheinungsdatum: 17.02.2023
Gewicht: 1,572 kg
Artikel-ID: 123641519
Warnhinweis

Ähnliche Produkte

Ähnliche Produkte