1Chapter 1. Introduction
696.7 Neural networks and neuroscience
21.1 Introduction to Data Science
706.8 Summary
31.2 Introduction to Machine learning
716.8 Questions
41.3 Introduction to Data Mining
72Chapter 7. Methods For Comparison
51.4 Introduction to Neural Network
737.1 Introduction
61.5 Introduction to Statistical classification
747.2 Materials and Methods
71.6 Summary
757.3. Classification of Algorithms
81.7 Questions
767.4. Analysis of Classification Algorithms
9Chapter 2. Classifications In Machine Learning
777.5. Determination of the Variables Causing
102.1 Classification in Machine Learning
78Default Risk
112.2 Classification Terminologies In Machine
797.6. Results and Discussion (Age Variable
12Learning
80Excluded)
132.3 Types Of Learners In Classification
817.7 Summary
142.4 Classification Algorithms
827.8 Questions
152.5 Artificial Neural Networks
83Chapter 8. Review Of Previous Empirical Comparisons
162.6 Support Vector Machine
848.1 Introduction
172.7 Classifier Evaluation
858.2 Study aim
182.8 Summary
868.3 Methods
192.9 Questions
878.4 Summary
20Chapter 3. Classical Statistical Methods
888.5 Questions
213.1 Introduction
89Chapter 9. Descriptive Statistics
223.2 Statistical Issues In Data Mining
909.1 What Are Descriptive Statistics?
233.3 Modeling Relationships using Regression
919.2 Univariate Analysis
24 Models
929.3 Summary
25Hypotheses Testing
939.4 Questions
263.5 Model (Variables or Features) Selection
94Chapter 10. Knowledge Representation
27using FDR Penalization in GLM
9510.1 Histograms
283.6 Summary
9610.2 Data Visualization
293.7 Questions
9710.3 Chernoff’s faces
30Chapter 4. Modern Statistical Techniques
9810.4 Pre-processing of the info
314.1 Introduction
9910.5 Summary
324.1 Linear regression
10010.6 Questions
334.2 Linear Regression with Information Scores
101Chapter 11. Learning To Control Dynamic Systems
344.3 Modeling with Genetic Algorithms
10211.1 Introduction
354.4 Molecular Spectroscopy Theory
10311.2 Background
364.5 Regression of Power Series
10411.3 The Problem: Analyzing Learning Dynamics
374.6 Scored Regression in Spectroscopy
105in Multi-agent Context
384.7 Genetic Algorithms for Hyperellipsoidal
10611.3 Case Study: IPD
39Clustering
10711.3 Methodology
404.8 Genetic Algorithm with Regularized
10811.4 Case Study: Analysis of Cooperation in
41Mahalanobis Distance (GARM)
109IPD Game
424.9 Summary
11011.5 Related Work
434.10 Questions
11111.6 Summary
44Chapter 5. Rules And Trees In Machine Learning
11211.7 Questions
455.1 How can an algorithm be represented as
113Chapter 12. Algorithm Sources And Details In Data Mining
46a tree?
11412.1 Introduction
475.2 Types of Decision Trees
11512.2 Types of Data
485.3 Advantages of Tree-based Machine Learning
11612.3 Data Mining Architecture
49Methods
11712.4 Steps involved in KDD Process
505.4 Disadvantages of Tree-based Machine
11812.5 Data Mining Implementation Process
51Learning Methods
11912.6 Data Mining Techniques
525.5 Common Terminology
12012.7 Challenges of Implementation of
535.6 Algorithms in Tree-based Machine Learning
121Data Mining
54Models
12212.8 Data mining Examples
555.7 Classification and Regression Tree (CART)
12312.9 Data Mining Tools
56Training Algorithm
12412.10 Data Mining Tasks
575.8 Regularization of Hyperparameters
12512.11 Mining of Frequent Patterns
585.9 Pruning in Tree-based Algorithms
12612.12 Classification and Prediction
595.10 Random Forest and Ensemble Learning
12712.13 Data Mining Issues
605.11 Decision Rules
12812.14 Mining Methodology and User Interaction
615.12 Summary
129Issues
625.13 Questions
13012.15 Evaluation of Data Mining
63Chapter 6. Neural Networks
13112.16 Data Mining System Products
646.1 Introduction
13212.17 Trends in data mining
656.2 Understanding neural networks
13312.18 Technology Trends in data mining
666.3 Neural networks in business
13412.19 Summary
676.4 Basics of Neural Networks
13512.20 Questions
686.6 Application of Neural Networks
136Glossary
