Artificial Intelligence With an Introduction to Machine Learning 2nd Edition by Richard Neapolitan, Xia Jiang 1351384391 9781351384391

Artificial Intelligence With an Introduction to Machine Learning 2nd Edition by Richard E. Neapolitan, Xia Jiang  – Ebook PDF Instant Download/DeliveryISBN: 1351384391, 9781351384391

Full download Artificial Intelligence With an Introduction to Machine Learning 2nd Edition after payment.

Product details:

ISBN-10 :  1351384391 

ISBN-13 :  9781351384391

Author:  Richard E. Neapolitan, Xia Jiang 

The first edition of this popular textbook, Contemporary Artificial Intelligence, provided an accessible and student friendly introduction to AI. This fully revised and expanded update, Artificial Intelligence: With an Introduction to Machine Learning, Second Edition, retains the same accessibility and problem-solving approach, while providing new material and methods. The book is divided into five sections that focus on the most useful techniques that have emerged from AI. The first section of the book covers logic-based methods, while the second section focuses on probability-based methods. Emergent intelligence is featured in the third section and explores evolutionary computation and methods based on swarm intelligence. The newest section comes next and provides a detailed overview of neural networks and deep learning. The final section of the book focuses on natural language understanding. Suitable for undergraduate and beginning graduate students, this class-tested textbook provides students and other readers with key AI methods and algorithms for solving challenging problems involving systems that behave intelligently in specialized domains such as medical and software diagnostics, financial decision making, speech and text recognition, genetic analysis, and more.

Artificial Intelligence With an Introduction to Machine Learning 2nd Table of contents:

Chapter 1 Introduction to Arti cial Intelligence
1.1 History of Articial Intelligence
1.1.1 What Is Articial Intelligence
1.1.2 Emergence of AI
1.1.3 Cognitive Science and AI
1.1.5 Knowledge-Based Systems
1.1.4 Logical Approach to AI
1.1.6 Probabilistic Approach to AI
1.1.7 Evolutionary Computation and Swarm Intelligence
1.1.8 Neural Networks & Deep Learning
1.1.9 A Return to Creating HAL
1.2 Outline of This Book
Part I Logical Intelligence
Chapter 2 Propositional Logic
2.1 Basics of Propositional Logic
2.1.1 Syntax
2.1.2 Semantics
2.1.3 Tautologies and Logical Implication
2.1.4 Logical Arguments
2.1.5 Derivation Systems
2.2 Resolution
2.2.1 Normal Forms
2.2.2 Derivations Using Resolution
2.2.3 Resolution Algorithm
2.3 Articial Intelligence Applications
2.3.1 Knowledge-Based Systems
2.3.2 Wumpus World
2.4 Discussion and Further Reading
Chapter 3 First-Order Logic
3.1 Basics of First-Order Logic
3.1.1 Syntax
3.1.2 Semantics
3.1.3 Validity and Logical Implication
3.1.4 Derivation Systems
3.1.5 Modus Ponens for First-Order Logic
3.2 Articial Intelligence Applications
3.2.1 Wumpus World Revisited
3.2.2 Planning
3.3 Discussion and Further Reading
Chapter 4 Certain Knowledge Representation
4.1 Taxonomic Knowledge
4.1.1 Semantic Nets
4.1.2 Model of Human Organization of Knowledge
4.2 Frames
4.2.1 Frame Data Structure
4.2.2 Planning a Trip Using Frames
4.3 Nonmonotonic Logic
4.3.1 Circumscription
4.3.2 Default Logic
4.3.3 Diculties
4.4 Discussion and Further Reading
Chapter 5 Learning Deterministic Models
5.1 Supervised Learning
5.2 Regression
5.2.1 Simple Linear Regression
5.2.3 Overtting and Cross Validation
5.2.2 Multiple Linear Regression
5.3 Parameter Estimation
5.3.1 Estimating the Parameters for Simple Linear Regression
5.3.2 Gradient Descent
5.3.3 Logistic Regression and Gradient Descent
5.3.4 Stochastic Gradient Descent
5.4 Learning a Decision Tree
5.4.1 Information Theory
5.4.2 Information Gain and the ID3 Algorithm
5.4.3 Overtting
Part II Probabilistic Intelligence
Chapter 6 Probability
6.1 Probability Basics
6.1.1 Probability Spaces
6.1.2 Conditional Probability and Independence
6.1.3 Bayes’ Theorem
6.2 Random Variables
6.2.1 Probability Distributions of Random Variables
6.2.2 Independence of Random Variables
6.3 Meaning of Probability
6.3.1 Relative Frequency Approach to Probability
6.3.2 Subjective Approach to Probability
6.4 Random Variables in Applications
6.5 Probability in the Wumpus World
Chapter 7 Uncertain Knowledge Representation
7.1 Intuitive Introduction to Bayesian Networks
7.2 Properties of Bayesian Networks
7.2.1 Denition of a Bayesian Network
7.2.2 Representation of a Bayesian Network
7.3 Causal Networks as Bayesian Networks
7.3.1 Causality
7.3.2 Causality and the Markov Condition
7.3.3 Markov Condition without Causality
7.4 Inference in Bayesian Networks
7.4.1 Examples of Inference
7.4.2 Inference Algorithms and Packages
7.4.3 Inference Using Netica
7.5 Networks with Continuous Variables
7.5.1 Gaussian Bayesian Networks
7.5.2 Hybrid Networks
7.6 Obtaining the Probabilities
7.6.1 Diculty Inherent in Multiple Parents
7.6.2 Basic Noisy OR-Gate Model
7.6.3 Leaky Noisy OR-Gate Model
7.6.4 Further Models
7.7 Large-Scale Application: Promedas
Chapter 8 Advanced Properties of Bayesian Networks
8.1 Entailed Conditional Independencies
8.1.1 Examples of Entailed Conditional Independencies
8.1.2 d-Separation
8.2 Faithfulness
8.2.1 Unfaithful Probability Distributions
8.2.2 Faithfulness Condition
8.3 Markov Equivalence
8.4 Markov Blankets and Boundaries
Chapter 9 Decision Analysis
9.1 Decision Trees
9.1.1 Simple Examples
9.1.2 Solving More Complex Decision Trees
9.2 In uence Diagrams
9.2.1 Representing Decision Problems with In uence Diagrams
9.2.2 Solving In uence Diagrams
9.2.3 Techniques for Solving In uence Diagrams
9.2.4 Solving In uence Diagrams Using Netica
9.3 Modeling Risk Preferences
9.3.1 Exponential Utility Function
9.3.2 Assessing r
9.4 Analyzing Risk Directly
9.4.1 Using the Variance to Measure Risk
9.4.2 Risk Proles
9.4.3 Dominance
9.5 Good Decision versus Good Outcome
9.6 Sensitivity Analysis
9.7 Value of Information
9.7.1 Expected Value of Perfect Information
9.7.2 Expected Value of Imperfect Information
9.8 Discussion and Further Reading
9.8.1 Academics
9.8.2 Business and Finance
9.8.3 Capital Equipment
9.8.4 Computer Games
9.8.5 Computer Vision
9.8.6 Computer Software
9.8.7 Medicine
9.8.8 Natural Language Processing
9.8.9 Planning
9.8.10 Psychology
9.8.11 Reliability Analysis
9.8.12 Scheduling
9.8.13 Speech Recognition
9.8.14 Vehicle Control and Malfunction Diagnosis
Chapter 10 Learning Probabilistic Model Parameters
10.1 Learning a Single Parameter
10.1.1 Binomial Random Variables
10.1.2 Multinomial Random Variables
10.2 Learning Parameters in a Bayesian Network
10.2.1 Procedure for Learning Parameters
10.2.2 Equivalent Sample Size
10.3 Learning Parameters with Missing DataF
Chapter 11 Learning Probabilistic Model Structure
11.1 Structure Learning Problem
11.2 Score-Based Structure Learning
11.2.1 Bayesian Score
11.2.2 BIC Score
11.2.3 Consistent Scoring Criteria
11.2.4 How Many DAGs Must We Score
11.2.5 Using the Learned Network to Do Inference
11.2.6 Learning Structure with Missing DataF
11.2.7 Approximate Structure Learning
11.2.8 Model Averaging
11.2.9 Approximate Model AveragingF
11.3 Constraint-Based Structure Learning
11.3.1 Learning a DAG Faithful to P
11.3.2 Learning a DAG in which P Is Embedded Faithfully
11.4 Application: MENTOR
11.4.1 Developing the Network
11.4.2 Validating MENTOR
11.5 Software Packages for Learning
11.6 Causal Learning
11.6.1 Causal Faithfulness Assumption
11.6.2 Causal Embedded Faithfulness Assumption
11.6.3 Application: College Student Retention Rate
11.7 Class Probability Trees
11.7.1 Theory of Class Probability Trees
11.7.2 Application to Targeted Advertising
11.8 Discussion and Further Reading
11.8.1 Biology
11.8.2 Business and Finance
11.8.3 Causal Learning
11.8.4 Data Mining
11.8.5 Medicine
11.8.6 Weather Forecasting
Chapter 12 Unsupervised Learning and Reinforcement Learning
12.1 Unsupervised Learning
12.1.1 Clustering
12.1.2 Automated Discovery
12.2 Reinforcement Learning
12.2.1 Multi-Armed Bandit Algorithms
12.2.2 Dynamic NetworksF
12.3 Discussion and Further Reading
Part III Emergent Intelligence
Chapter 13 Evolutionary Computation
13.1 Genetics Review
13.2 Genetic Algorithms
13.2.1 Algorithm
13.2.2 Illustrative Example
13.2.3 Traveling Salesperson Problem
13.3 Genetic Programming
13.3.1 Illustrative Example
13.3.2 Articial Ant
13.3.3 Application to Financial Trading
13.4 Discussion and Further Reading
Chapter 14 Swarm Intelligence
14.1 Ant System
14.1.1 Real Ant Colonies
14.1.2 Articial Ants for Solving the TSP
14.2 Flocks
14.3 Discussion and Further Reading
Part IV Neural Intelligence
Chapter 15 Neural Networks and Deep Learning
15.1 The Perceptron
15.1.1 Learning the Weights for a Perceptron
15.1.2 The Perceptron and Logistic Regression
15.2 Feedforward Neural Networks
15.2.1 Modeling XOR
15.2.2 Example with Two Hidden Layers
15.2.3 Structure of a Feedforward Neural Network
15.3 Activation Functions
15.3.1 Output Nodes
15.3.2 Hidden Nodes
15.4 Application to Image Recognition
15.5 Discussion and Further Reading
Part V Language Understanding
Chapter 16 Natural Language Understanding
16.1 Parsing
16.1.1 Recursive Parser
16.1.2 Ambiguity
16.1.3 Dynamic Programming Parser
16.1.4 Probabilistic Parser
16.1.5 Obtaining Probabilities for a PCFG
16.1.6 Lexicalized PCFG
16.2 Semantic Interpretation
16.3 Concept/Knowledge Interpretation
16.4 Information Extraction
16.4.1 Applications of Information Extraction
16.4.2 Architecture for an Information Extraction System
16.5 Discussion and Further Reading

People also search for Artificial Intelligence With an Introduction to Machine Learning 2nd:

what is artificial intelligence introduction
 
artificial intelligence brief introduction
 
artificial intelligence laws
 
artificial intelligence used in daily life
 
artificial intelligence summary

Tags: Artificial Intelligence, an Introduction, Machine Learning, Richard Neapolitan, Xia Jiang