An Introduction to Artificial Intelligence and Machine Learning I: By day-to-day examples

Description

How does our brain work in our routine life? The same way we design artificial intelligence in machines. Instead of complex straightforward theory, this book explains all logic and algorithms with the help of day-to-day examples. The language is straightforward. Besides, the examples are straightforward. We adequately cover all functions of the intelligent agent and machine learning models. This book is a sweet friend for newcomers to the AI field (this includes academic students and working professionals.). This book additionally includes statistical models. The overall intention of this book is to spread the knowledge to all kinds of readers preparing themselves to secure a visa for the upcoming AI-driven earth. Cover Title Copyright Preface Chapter 1. Introduction to Artificial Intelligence and Machine Learning 1.1 The Level and Depth of the Subjects in This Book 1.2 Is This Book Complex to Read? 1.3 Details About Other Volumes of This Book Series 1.4 Artificial Intelligence 1.5 Statistical Methods 1.6 Machine Learning 1.7 Summary Part 1. Artificial Intelligence Chapter 2. Artificial Intelligence: Introduction 2.1 Rationality Chapter 3. Artificial Intelligence: Search and Problem Solving 3.1 Problem-Solving Introduction 3.2 Find the Goal and Formulate the Goal 3.3 Design the Problem and Problem Formulation 3.4 Search and Executions 3.5 Steps Involved in Problem-Solving Agent Algorithm 3.6 Some Crucial Components of the Problem 3.7 Real-Time Examples for Problem-solving 3.8 Tree and Graph 3.9 Problem-Solving Performance 3.10 Searching 3.11 Uninformed Search Strategies 3.12 Breadth-First Search 3.13 Depth-First Search 3.14 Uniform Cost Search 3.15 Depth-Limited Search 3.16 Iterative Deepening Depth-First Search (IDDFS) 3.17 Bidirectional Search 3.18 Performance Comparison – Uninformed Search Algorithms 3.19 Informed Search Algorithms 3.20 Greedy Best-First Search 3.21 A* Search Algorithm 3.22 Admissibility and Consistency 3.23 Iterative Deepening A* Algorithm (IDA*) Chapter 4. Artificial Intelligence: Local Search 4.1 Introduction to Local Search 4.2 Hill Climbing Local Search Algorithm 4.3 Simulated Annealing 4.4 Local Beam Search 4.5 Genetic Algorithms Chapter 5. Artificial Intelligence: Adversarial Search – Games 5.1 Game Theory 5.2 The Minimax Algorithm 5.3 Alpha-Beta Pruning Chapter 6. Artificial Intelligence: Logic and logical agents 6.1 Knowledge-Based Agents 6.2 Logic 6.3 Logical Reasoning 6.4 Logical Inference 6.5 Inference Algorithm and Its Properties 6.6 Propositional Logic 6.7 Syntax of Propositional Logic 6.8 Semantics 6.9 Example Knowledge Base Using Propositional Logic 6.10 Inference Algorithm Logic, Theorems Chapter 7. Artificial Intelligence: Uncertainty 7.1 What is Uncertainty, and How is It Useful? 7.2 Basics of Probability 7.3 Logic vs. Probability 7.4 Probability: [Probability from Mathematics] 7.5 Probability in Artificial Intelligence 7.6 The Prior or Unconditional Probability 7.7 Posterior or Conditional Probability 7.8 Probability Distribution 7.9 Joint Probability Distribution 7.10 Full Joint Inference and Inference 7.11 Inference by Enumeration 7.12 Independence 7.13 Conditional Independence 7.14 Bayes Theorem and Naïve Bayes 7.15 Bayesian Networks: Syntax 7.16 Bayesian Networks: Factorization 7.17 Inference in Bayesian Networks 7.18 Bayesian Network: Conditional Independence 7.19 Markov Blanket 7.20 Definition (D-Separation) 7.21 Bayesian Network: Inference Using Enumeration and Variable Elimination 7.21.1 Inference using enumeration 7.21.2 Variable elimination method 7.22 Bayesian Network: Rejection Sampling 7.23 Bayesian Network: Likelihood Weighting 7.24 Bayesian Network: Maximum Likelihood 7.25 Bayesian Network: Maximum a Posteriori (MAP) Learning Chapter 8. Artificial Intelligence: Top View – Agent and Environments 8.1 Agent in General 8.2 Task Environment and PEAS Description 8.3 Properties of the Task Environment (Characteristics) 8.4 Agent Program Types 8.5 Simple Reflex Agents 8.6 Model-Based Reflex Agents 8.7 Goal-Based Agents 8.8 Utility-Based Agents 8.9 General Learning Agents Chapter 9. Artificial Intelligence: Ethics 9.1 Basics 9.2 Robustness of AI Systems 9.3 Transparency of AI Systems 9.4 Data Bias 9.5 Accountability for Ethics Issues 9.6 Data Privacy 9.7 Cyber Security with AI and ML Systems Part 2. Statistical Methods Chapter 10. Statistical Methods: Statistics and Probability Basics 10.1 Data and Data Visualization 10.2 Central Tendency 10.3 Mean 10.4 Median 10.5 Mode 10.6 Measures of Spread 10.7 Range 10.8 Interquartile Range (IQR) 10.9 Data Set Value Changes and Outliers 10.10 Constant Addition or Subtraction 10.11 Extreme Values 10.12 Box and Whisker Plots 10.13 Sample Mean, Variance, and Standard Deviation 10.13.1 Sample Mean 10.13.2 Variance 10.13.3 Standard Deviation 10.14 Frequency Histogram and Density Curve 10.15 Symmetric Distribution 10.16 Skewed Distributions 10.17 Outlier Calculations 10.18 Normal Distribution 10.19 Z-Score 10.20 Probability 10.21 Probability – Addition, Union, and Intersection Chapter 11. Statistical Methods: Independent probability 11.1 The Multiplication Rule 11.2 Dependent Probability 11.3 Bayes’ Theorem Chapter 12. Statistical Methods: Discrete Random Variables 12.1 Discrete Random Variables and Probability Distributions 12.2 Additional use of Random Variables 12.3 Expected Value 12.4 Variance and Standard Deviation 12.5 Transforming Random Variables 12.6 Linear Combinations of Random Variables 12.7 Permutations and Combinations 12.8 Binomial Random Variables 12.9 Binomial Random Variable Characters 12.10 Binomial Probability 12.11 Poisson Distributions 12.12 Bernoulli Random Variables Chapter 13. Statistical Methods: Sampling 13.1 The Art of Collecting Statistical Data 13.2 The Goal of Collecting Samples 13.3 Observational Study and Experimental Study 13.4 One-Way Tables and Two-Way Tables 13.5 Exclusive to Experimental Studies 13.6 Sampling and Bias 13.7 Sampling Techniques 13.8 Sampling Distributions of the Sample Mean 13.9 Sampling Distribution of the Sample Proportion (SDSP) 13.10 The Student’s t-Distribution 13.11 Confidence Interval for the Mean 13.12 Confidence Interval for the Proportion Chapter 14. Statistical Methods: Hypothesis Testing 14.1 Inferential Statistics and Hypotheses 14.2 Hypothesis 14.3 The Population Mean μ 14.4 For Population Proportions 14.5 Significance Level and Type I and II Errors 14.6 Test Statistics for One- and Two-Tailed Tests 14.7 Choosing a One-Tailed or Two-Tailed Test 14.8 The Α Value for One- and Two-Tailed Tests 14.9 Calculating the Test Statistic 14.10 The p-Value and Rejecting the Null 14.11 Significance 14.12 Hypothesis Testing for the Population Proportion Part. 3 Machine Learning Chapter 15. Machine Learning: Introduction 15.1 Machine Learning 15.2 Types of Machine Learning 15.3 Visualization Examples for Types of Machine Learning 15.4 Training, Validation, and Testing Data Sets Chapter 16. Machine Learning: Data Workflow and Data Mining 16.1 Definition of Data 16.2 Types of Attributes 16.3 Discrete and Continuous Attributes 16.4 Characteristics of Data 16.5 Outliers 16.6 Data Quality Problems 16.7 Data Pre-Processing 16.8 Confusion Matrix – Performance Evaluation 16.9 Receiver Operating Characteristic Curve 16.10 Dealing with Imbalanced Classes 16.11 Challenges of Machine Learning 16.12 Bias-Variance Trade-Off 16.13 Choice of Hyperparameters Chapter 17. Machine Learning: Linear Regression Models 17.1 Linear Regression 17.2 Other Examples of Linear Regression 17.3 How Exactly the Machine Learning Model Learns (Model Engine) 17.4 Cost Function 17.5 Linear Functions vs. Nonlinear Functions 17.6 Optimization and Optimization Functions 17.7 Required Mathematics and Statistics for Machine Learning Linear Regression 17.8 Traditional “Closed from Solution” Mathematics Model for Optimization 17.9 Overfitting and Bias-Variance Trade-Off 17.10 L1 and L2 Regularization Methods 17.11 Earlier Stopping Chapter 18. Machine Learning: Classification (Linear and Logistic classification) 18.1 Classification 18.2 Classification Types 18.2.1 Linear Classifier 18.2.2 Nonlinear Classifier 18.3 Logistic Regression 18.4 Binary Classification vs. Multi Classification 18.5 Cost Function 18.6 Cross Entropy 18.7 Cost Optimization Chapter 19. Machine Learning: Decision Tree 19.1 Decision Tree 19.2 Decision Tree – Example 19.3 Pruning 19.4 How to Create the Decision Tree? 19.5 Gini Impurity 19.6 Entropy and Information Gain 19.7 Information Gain (IG) 19.8 Issues in the Decision Tree Model Chapter 20. Machine Learning: Instance-based Learning Algorithms 20.1 K-nearest Neighbour Classifier 20.2 Steps involved in KNN Algorithm Models 20.3 K-Elbow Method for ‘K’ Value Selection 20.4 Locally Weighted Regression Model Chapter 21. Machine Learning: Support Vector Machine 21.1 SVM Basics 21.2 Maximum Margin Classifier 21.3 Soft Margin Classifier 21.4 Two-Dimensional Data and Support Vector Classifier 21.5 Linear Classification Mathematics 21.6 Three-Dimension Support Vector Classifications 21.7 Mathematics Behind Linear Support Vectors 21.8 Support Vector Machine Logic 21.9 Kernal Functions Chapter 22. Machine Learning: Bayesian Learning 22.1 Bayesian Machine Learning 22.2 Bayes’ Theorem (Statistics Point of View) 22.3 Bayes Theorem and Naïve Bayes from Artificial Intelligence’s Point of View 22.4 Naïve Bayes Classifier in Machine Learning 22.5 Maximum Likelihood Estimation 22.6 Maximum A Posteriori (MAP) Learning Chapter 23. Machine Learning: Ensemble Learning 23.1 Bagging and Boosting 23.2 Random Forest Algorithm 23.3 AdaBoost Algorithm 23.4 Steps involved in Ada Boosting 23.5 Gradient Boosting Algorithms 23.6 XGBoost Algorithms Chapter 24. Machine Learning: Unsupervised Learning 24.1 Clustering 24.2 Association 24.3 K-Means Clustering 24.4 How to Decide the “K” Value 24.5 K-Means Clustering for a Two-Dimensional Dataset About the Author