Note: Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- I: Fundamentals -- Chapter 1: Introduction to Machine Learning -- 1.1 Supervised learning -- 1.1.1 Regression problems -- 1.1.2 Classification problems -- 1.2 Unsupervised learning -- 1.3 Roadmap -- 1.4 The data sets -- Chapter 2: Modeling Process -- 2.1 Prerequisites -- 2.2 Data splitting -- 2.2.1 Simple random sampling -- 2.2.2 Stratified sampling -- 2.2.3 Class imbalances -- 2.3 Creating models in R -- 2.3.1 Many formula interfaces -- 2.3.2 Many engines -- 2.4 Resampling methods -- 2.4.1 k-fold cross validation -- 2.4.2 Bootstrapping -- 2.4.3 Alternatives -- 2.5 Bias variance trade-off -- 2.5.1 Bias -- 2.5.2 Variance -- 2.5.3 Hyperparameter tuning -- 2.6 Model evaluation -- 2.6.1 Regression models -- 2.6.2 Classification models -- 2.7 Putting the processes together -- Chapter 3: Feature & -- Target Engineering -- 3.1 Prerequisites -- 3.2 Target engineering -- 3.3 Dealing with missingness -- 3.3.1 Visualizing missing values -- 3.3.2 Imputation -- 3.4 Feature filtering -- 3.5 Numeric feature engineering -- 3.5.1 Skewness -- 3.5.2 Standardization -- 3.6 Categorical feature engineering -- 3.6.1 Lumping -- 3.6.2 One-hot & -- dummy encoding -- 3.6.3 Label encoding -- 3.6.4 Alternatives -- 3.7 Dimension reduction -- 3.8 Proper implementation -- 3.8.1 Sequential steps -- 3.8.2 Data leakage -- 3.8.3 Putting the process together -- II: Supervised Learning -- Chapter 4: Linear Regression -- 4.1 Prerequisites -- 4.2 Simple linear regression -- 4.2.1 Estimation -- 4.2.2 Inference -- 4.3 Multiple linear regression -- 4.4 Assessing model accuracy -- 4.5 Model concerns -- 4.6 Principal component regression -- 4.7 Partial least squares -- 4.8 Feature interpretation -- 4.9 Final thoughts -- Chapter 5: Logistic Regression -- 5.1 Prerequisites. , 5.2 Why logistic regression -- 5.3 Simple logistic regression -- 5.4 Multiple logistic regression -- 5.5 Assessing model accuracy -- 5.6 Model concerns -- 5.7 Feature interpretation -- 5.8 Final thoughts -- Chapter 6: Regularized Regression -- 6.1 Prerequisites -- 6.2 Why regularize? -- 6.2.1 Ridge penalty -- 6.2.2 Lasso penalty -- 6.2.3 Elastic nets -- 6.3 Implementation -- 6.4 Tuning -- 6.5 Feature interpretation -- 6.6 Attrition data -- 6.7 Final thoughts -- Chapter 7: Multivariate Adaptive Regression Splines -- 7.1 Prerequisites -- 7.2 The basic idea -- 7.2.1 Multivariate adaptive regression splines -- 7.3 Fitting a basic MARS model -- 7.4 Tuning -- 7.5 Feature interpretation -- 7.6 Attrition data -- 7.7 Final thoughts -- Chapter 8: K-Nearest Neighbors -- 8.1 Prerequisites -- 8.2 Measuring similarity -- 8.2.1 Distance measures -- 8.2.2 Preprocessing -- 8.3 Choosing k -- 8.4 MNIST example -- 8.5 Final thoughts -- Chapter 9: Decision Trees -- 9.1 Prerequisites -- 9.2 Structure -- 9.3 Partitioning -- 9.4 How deep? -- 9.4.1 Early stopping -- 9.4.2 Pruning -- 9.5 Ames housing example -- 9.6 Feature interpretation -- 9.7 Final thoughts -- Chapter 10: Bagging -- 10.1 Prerequisites -- 10.2 Why and when bagging works -- 10.3 Implementation -- 10.4 Easily parallelize -- 10.5 Feature interpretation -- 10.6 Final thoughts -- Chapter 11: Random Forests -- 11.1 Prerequisites -- 11.2 Extending bagging -- 11.3 Out-of-the-box performance -- 11.4 Hyperparameters -- 11.4.1 Number of trees -- 11.4.2 mtry -- 11.4.3 Tree complexity -- 11.4.4 Sampling scheme -- 11.4.5 Split rule -- 11.5 Tuning strategies -- 11.6 Feature interpretation -- 11.7 Final thoughts -- Chapter 12: Gradient Boosting -- 12.1 Prerequisites -- 12.2 How boosting works -- 12.2.1 A sequential ensemble approach -- 12.2.2 Gradient descent -- 12.3 Basic GBM -- 12.3.1 Hyperparameters. , 12.3.2 Implementation -- 12.3.3 General tuning strategy -- 12.4 Stochastic GBMs -- 12.4.1 Stochastic hyperparameters -- 12.4.2 Implementation -- 12.5 XGBoost -- 12.5.1 XGBoost hyperparameters -- 12.5.2 Tuning strategy -- 12.6 Feature interpretation -- 12.7 Final thoughts -- Chapter 13: Deep Learning -- 13.1 Prerequisites -- 13.2 Why deep learning -- 13.3 Feedforward DNNs -- 13.4 Network architecture -- 13.4.1 Layers and nodes -- 13.4.2 Activation -- 13.5 Backpropagation -- 13.6 Model training -- 13.7 Model tuning -- 13.7.1 Model capacity -- 13.7.2 Batch normalization -- 13.7.3 Regularization -- 13.7.4 Adjust learning rate -- 13.8 Grid search -- 13.9 Final thoughts -- Chapter 14: Support Vector Machines -- 14.1 Prerequisites -- 14.2 Optimal separating hyperplanes -- 14.2.1 The hard margin classifier -- 14.2.2 The soft margin classifier -- 14.3 The support vector machine -- 14.3.1 More than two classes -- 14.3.2 Support vector regression -- 14.4 Job attrition example -- 14.4.1 Class weights -- 14.4.2 Class probabilities -- 14.5 Feature interpretation -- 14.6 Final thoughts -- Chapter 15: Stacked Models -- 15.1 Prerequisites -- 15.2 The Idea -- 15.2.1 Common ensemble methods -- 15.2.2 Super learner algorithm -- 15.2.3 Available packages -- 15.3 Stacking existing models -- 15.4 Stacking a grid search -- 15.5 Automated machine learning -- Chapter 16: Interpretable Machine Learning -- 16.1 Prerequisites -- 16.2 The idea -- 16.2.1 Global interpretation -- 16.2.2 Local interpretation -- 16.2.3 Model-specific vs. model-agnostic -- 16.3 Permutation-based feature importance -- 16.3.1 Concept -- 16.3.2 Implementation -- 16.4 Partial dependence -- 16.4.1 Concept -- 16.4.2 Implementation -- 16.4.3 Alternative uses -- 16.5 Individual conditional expectation -- 16.5.1 Concept -- 16.5.2 Implementation -- 16.6 Feature interactions -- 16.6.1 Concept. , 16.6.2 Implementation -- 16.6.3 Alternatives -- 16.7 Local interpretable model-agnostic explanations -- 16.7.1 Concept -- 16.7.2 Implementation -- 16.7.3 Tuning -- 16.7.4 Alternative uses -- 16.8 Shapley values -- 16.8.1 Concept -- 16.8.2 Implementation -- 16.8.3 XGBoost and built-in Shapley values -- 16.9 Localized step-wise procedure -- 16.9.1 Concept -- 16.9.2 Implementation -- 16.10 Final thoughts -- III: Dimension Reduction -- Chapter 17: Principal Components Analysis -- 17.1 Prerequisites -- 17.2 The idea -- 17.3 Finding principal components -- 17.4 Performing PCA in R -- 17.5 Selecting the number of principal components -- 17.5.1 Eigenvalue criterion -- 17.5.2 Proportion of variance explained criterion -- 17.5.3 Scree plot criterion -- 17.6 Final thoughts -- Chapter 18: Generalized Low Rank Models -- 18.1 Prerequisites -- 18.2 The idea -- 18.3 Finding the lower ranks -- 18.3.1 Alternating minimization -- 18.3.2 Loss functions -- 18.3.3 Regularization -- 18.3.4 Selecting k -- 18.4 Fitting GLRMs in R -- 18.4.1 Basic GLRM model -- 18.4.2 Tuning to optimize for unseen data -- 18.5 Final thoughts -- Chapter 19: Autoencoders -- 19.1 Prerequisites -- 19.2 Undercomplete autoencoders -- 19.2.1 Comparing PCA to an autoencoder -- 19.2.2 Stacked autoencoders -- 19.2.3 Visualizing the reconstruction -- 19.3 Sparse autoencoders -- 19.4 Denoising autoencoders -- 19.5 Anomaly detection -- 19.6 Final thoughts -- IV: Clustering -- Chapter 20: K-means Clustering -- 20.1 Prerequisites -- 20.2 Distance measures -- 20.3 Defining clusters -- 20.4 k-means algorithm -- 20.5 Clustering digits -- 20.6 How many clusters? -- 20.7 Clustering with mixed data -- 20.8 Alternative partitioning methods -- 20.9 Final thoughts -- Chapter 21: Hierarchical Clustering -- 21.1 Prerequisites -- 21.2 Hierarchical clustering algorithms -- 21.3 Hierarchical clustering in R. , 21.3.1 Agglomerative hierarchical clustering -- 21.3.2 Divisive hierarchical clustering -- 21.4 Determining optimal clusters -- 21.5 Working with dendrograms -- 21.6 Final thoughts -- Chapter 22: Model-based Clustering -- 22.1 Prerequisites -- 22.2 Measuring probability and uncertainty -- 22.3 Covariance types -- 22.4 Model selection -- 22.5 My basket example -- 22.6 Final thoughts -- Bibliography -- Index.

Note: Intro -- MOTOR IMAGERY: EMERGING PRACTICES, ROLE IN PHYSICAL THERAPY AND CLINICAL IMPLICATIONS -- MOTOR IMAGERY: EMERGING PRACTICES, ROLE IN PHYSICAL THERAPY AND CLINICAL IMPLICATIONS -- Library of Congress Cataloging-in-Publication Data -- Contents -- Preface -- Chapter 1: Spinal Neural Function during Motor Imagery -- Abstract -- 1. Introduction -- 2. Excitability of Spinal Neural Function during Several Motor Imagery Tasks Involving Isometric Opponens Pollicis Activity (Suzuki et al., 2013) -- 3. Excitability of Spinal Neural Function by Motor Imagery with Isometric Opponens Pollicis Activity: Influence of Vision during Motor Imagery (NeuroRehabilitation 34:725-729, 2014) -- 4. Excitability of Spinal Neural Function during Motor Imagery in Parkinson's Disease (PD) (Suzuki et al., 2014) -- Conclusion -- References -- Chapter 2: Motor Imagery in Children with Developmental Coordination Disorder -- Abstract -- Introduction -- Developmental Coordination Disorder (DCD) -- Internal Modeling Deficit Hypothesis (IMD) -- Motor Imagery (MI) -- Motor Imagery Studies in DCD -- Implications and Applications -- Conclusion -- References -- Chapter 3: The Potential of Motor Imagery Training in Fall Prevention among the Elderly -- Abstract -- Introduction -- Mentally Representing Action and Motor Imagery -- Motor Imagery Practice -- General Strategies for Motor Imagery Practice -- Conclusion -- References -- Chapter 4: Motor Imagery with Brain- Computer Interface Neurotechnology -- Abstract -- Introduction -- Methods -- Results -- Assessment of Cognitive Functions with Motor Imagery -- Discussion -- Future Outlook -- References -- Chapter 5: Mental Practice: Neurophysiology and Clinical Aspects in Neurorehabilitation -- Abstract -- 1. Motor Imagery and Mental Practice or Motor Imagery Practice. , 2. Neurophysiology: The Motor Imagery- Based Mental Practice Is Similar to Motor Execution -- 3. Emerging Practices -- 4. Role in Physical Therapy -- 5. Clinical Implications -- References -- Index -- Blank Page.

Note: Intro -- FRUCTOSE -- FRUCTOSE -- CONTENTS -- PREFACE -- UNHEALTHY DIET INTAKE DURING THE PERI-NATAL AND ADULT PERIODS: DETRIMENTAL NEUROENDOCRINE AND METABOLIC EFFECTS -- ABSTRACT -- 1. INTRODUCTION -- 2. MATERIALS AND METHODS -- 2.1. Experimental Design I: Impact of FRD Consumption by the Lactating Mother on Metabolic and Neuroendocrine Functions in the First Male Offspring in the Adult Age -- 2.1.1. Animals and Diets -- 2.1.2. Studies Performed in Basal Condition -- 2.1.3. Glucose Tolerance Test (GTT) -- 2.1.4. Hypothalamic Sensitivity to Leptin -- 2.1.5. Morphological Characteristics of the AT -- 2.1.6. Isolation and Incubation of RPAT Cells -- 2.1.7. Measurement of Peripheral Biomarkers -- 2.1.8. Tissue RNA Isolation and Real-Time PCR -- 2.1.9. Medial Basal Hypothalamus: Western Blot Analysis -- 2.2. Experimental Design II: Impact of FRD Consumption by the Naive Adult Male Rat -- 2.2.1. Animals and Diets -- 2.2.2. Chemicals -- 2.2.3. Peripheral Metabolite Measurements -- 2.2.4. Glucose Tolerance Test (GTT) -- 2.2.5. Histological Studies in AAT -- 2.2.6. Enzymatic Antioxidant Activities and Lipid Soluble Antioxidants in AAT -- 2.2.7. Thiobarbituric acid Reactive Substances (TBARS) Measurement -- 2.2.8. Lipid Composition in AAT -- 2.2.9. Leptin Release by AAT -- 2.2.10. Gene Expression in AAT -- 3. STATISTICAL ANALYSIS -- 4. RESULTS -- 4.1. Experiment 1: FRD Administration to Normal Lactating Mothers: Neuroendocrine and Metabolic Impact on the First Adult Male Offspring -- 4.1.1. Body Weight, Energy Intake and Peripheral Metabolites in Lactating Mothers -- 4.1.2. Impact of FRD Administration to Lactating Mothers upon Offspring BW, Food Intake and Peripheral Biomarkers -- 4.1.3. Peripheral Adipokine Profile in 60 Day-old Male CD and FRD Rats -- 4.1.4. GTT in Adult Male Offspring from CD or FRD Lactating Mothers. , 4.1.5. Adult Offspring RPAT: Morphological Characteristics and Function -- 4.1.6. Hypothalamic Circuitry Controlling Food Intake in Adult Male Rats -- 4.1.7. Leptin Sensitivity in the CD and FRD Offspring -- 4.2. Experiment II: Effect of Short-Time FRD Administration to Naive Adult Male Rats -- 4.2.1. Dietary Intake, BW and Peripheral Biomarkers in Adult CD and FRD Male Rats -- 4.2.2. FRD-induced Modifications in AAT Mass and Adipocyte Characteristics -- 4.2.3. Impact of FRD on AAT FA Composition/Release and Redox State -- 4.2.4. Adipokines, IRS-1 and IRS-2 mRNAs Expressions in AAT Pads -- 4.2.5. In Vitro Leptin Secretion by Isolated Adipocytes from CD and FRD Rats -- 5. DISCUSSION -- 5.1. Detrimental Long-term Effect of Feeding Lactating Mothers with a FRD upon the Offspring's Health -- 5.2. FRD Administration to Adult Rats and Adiposity Dysfunction -- CONCLUSION -- ACKNOWLEDGMENT -- REFERENCES -- HIGH-FRUCTOSE CONSUMPTION AND METABOLIC DISEASES -- ABSTRACT -- INTRODUCTION -- BIOCHEMICAL CHARACTERISTICS -- SOURCES OF FRUCTOSE -- FRUCTOSE CONSUMPTION -- METABOLIC ASPECTS OF FRUCTOSE -- Intestinal absorption -- Hepatic Metabolism -- First Steps of Fructose and Glucose Metabolism -- Metabolic Fate of Triose-P from Fructose -- Endogenous Fructose Production -- Ectopic Lipid Deposition -- FRUCTOSE AND EXERCISE -- EFFECTS OF DIETARY FRUCTOSE -- Dyslipidemia -- Obesity and Fructose -- Uric acid and Fructose -- Hypertension and Fructose -- Metabolic Syndrome / Insulin Resistance / Diabetes -- CONCLUSION -- REFERENCES -- FRUCTOSE AND NON-ALCOHOLIC FATTY LIVER DISEASE -- ABSTRACT -- INTRODUCTION -- FRUCTOSE CATABOLISM IN THE LIVER -- FRUCTOSE CONSUMPTION AND ITS IMPACT ON BODY MASS -- EFFECT OF FRUCTOSE ON INSULIN SENSITIVITY: -- IMPACT OF FRUCTOSE ON LEPTIN SIGNALING -- IMPACT OF FRUCTOSE ON GHRELIN SECRETION. , Fructose Consumption and Hepatic Inflammation -- HIGH FRUCTOSE CONSUMPTION AND CARDIOVASCULAR DISEASE -- ASSOCIATION OF FRUCTOSE CONSUMPTION WITH NON-ALCOHOLIC FATTY LIVER DISEASE -- MECHANISTIC INSIGHT INTO FRUCTOSE-INDUCED FATTY LIVER DISEASE -- CONCLUSION -- ACKNOWLEGMENT -- REFERENCES -- DETRIMENTAL EFFECTS OF EXCESSIVE FRUCTOSE INGESTION ON MEMORY AND OTHER BRAIN FUNCTIONS -- ABSTRACT -- INTRODUCTION -- PERIPHERAL EFFECTS -- CENTRAL EFFECTS -- POSSIBLE MECHANISMS -- PHYSIOLOGICAL RELEVANCE -- CONCLUSION -- REFERENCES -- ROLE OF FRUCTOSE IN BODY FUNCTIONS: AN OVERVIEW -- ABSTRACT -- INTRODUCTION -- Fructose Synthesis -- SPECIFIC FUNCTION OF FRUCTOSE -- Health Implication of Fructose -- Fructose and Insulin Resistance -- Fructose and Diabetes Mellitus -- Fructose and Obesity -- Fructose and Non Alcoholic Fatty Liver (NAFLD) -- Fructose and Metabolic Syndrome -- Fructose and Uric Acid -- Fructose, Advanced Glycation End-Products, and Aging -- Functional Bowel Disturbances -- CONCLUSION -- REFERENCES -- FRUCTOSE FACILITATES ALCOHOL METABOLISM -- DEFINITION, DISCOVERY AND PROBLEMS OF ALCOHOL -- ALCOHOL PHARMACOKINETICS -- The Oxidative Metabolism of Ethanol -- BIOCHEMICAL BASIS OF THE METABOLIC CONSEQUENCES OF ALCOHOL -- ALCOHOLISM AND RELATED PROBLEMS -- Clinical Presentations of Alcoholism -- TREATMENT OF ALCOHOL-INDUCED PROBLEMS -- FRUCTOSE ACCELERATES BLOOD ETHANOL ELIMINATION RATE (BEER) -- Mechanisms of the Stimulatory Effect of Fructose on Blood Ethanol Elimination Rate (BEER) -- Challenges of the "Fructose Effect" -- HONEY AND ALCOHOL METABOLISM -- REFERENCES -- FRUCTOSE AND THE CONTROL OF FOOD INTAKE -- ABSTRACT -- INTRODUCTION -- Fructose and Obesity Animal Models -- Sugar Solution Access and the Control of Food Intake -- The 11 HSD Hypothesis and Fructose -- mRNA in Liver and Mesenteric Adipose 11β-HSD-1 mRNA. , Sugar Solutions and the Hypothalamus -- Do Different Sugars Affect the Controls of Intake Differently? -- CONCLUSION -- ACKNOWLEDGMENT -- REFERENCES -- ABBREVIATIONS -- EFFECT OF FRUCTOSE ON HEALTH -- ABSTRACT -- INTRODUCTION -- BENEFICIAL EFFECTS OF FRUCTOSE -- Lactulose Stimulates the Growth of Health-Promoting Bacteria in the Gastrointestinal Tract, and Inhibits Growth of Pathogenic Bacteria -- Dietary Fructooligosaccharides and Potential Benefits on Health -- Calcium Fructoborate Reduces Exacerbated Cellular Immune Responses Induced by Fusarium Toxins -- DELETERIOUS EFFECTS OF FRUCTOSE -- The Role of Fructose-Enriched Diets in Mechanisms of Nonalcoholic Fatty Liver Disease -- Fructose: A Highly Lipogenic Nutrient Implicated in Insulin Resistance, Hepatic Steatosis, and the Metabolic Syndrome -- Fructose-Induced Increase in Ethanol Metabolism and the Risk of Syndrome X -- Metabolic and Behavioural Effects of Sucrose and Fructose/Glucose Drinks -- Fructose and Changes in Triglyceride or Body Weight -- A High-Fructose Diet Worsens Eccentric Left Ventricular Hypertrophy in Experimental Volume Overload -- Fructose and Hypertension -- Nutrition and Alzheimer's Disease: The Detrimental Role of a High Carbohydrate Diet -- Increased Fructose Intake as a Risk Factor for Dementia -- Aqueous Extract of Globularia Alypum Decreases Hypertriglyceridemia and Ameliorates Oxidative Status of the Muscle, Kidney, and Heart in Rats Fed a High-Fructose Diet -- Grape Seed Extract Supplementation Prevents High-Fructose Diet-Induced Insulin Resistance in Rats By Improving Insulin and Adiponectin Signalling Pathways -- Effects of a Maternal Diet Supplemented with Chocolate and Fructose Beverage during Gestation and Lactation on Rat Dams and their Offspring. , Maternal Fructose Intake during Pregnancy and Lactation Alters Placental Growth and Leads to Sex-Specific Changes in Fetal and Neonatal Endocrine Function -- DISCUSSION -- REFERENCES -- MAMMALIAN TRIOKINASE AND DIHYDROXYACETONE KINASE ARE THE SAME ENZYME -- ABSTRACT -- INTRODUCTION: FRUCTOSE METABOLISM -- THE REPORTED PROPERTIES OF TRIOKINASE -- DIHYDROXYACETONE KINASE -- "TRIOKINASE" IS THE SAME ENZYME AS DIHYDROXYACETONE KINASE -- CONCLUSION -- REFERENCES -- INDEX.