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Master in Artificial Intelligence
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- 3 High Quality Artificial Intelligence Books
- Easy language, to-the-point concepts
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Products Included:
Why Buy This Book?
- Evolution of AI: Books trace the journey of AI from theory to real-world applications.
- Global Perspectives: Provide insights into AI developments worldwide.
- Technical Basics: Explain machine learning and deep learning in accessible terms.
- Ethics: Explore ethical considerations in AI development and use.
What exactly do I get in this?
3 Pack Bundle
What’s Covered?
Table of content
● Introduction
✔ What Is Artificial Intelligence?
✔ Brain Science and Problem Solving
✔ The Turing Test and Chatterbots
✔ The History of AI
✔ The First Beginnings
✔ Logic Solves (Almost) All Problems
✔ The New Connectionism
✔ Reasoning Under Uncertainty
✔ Distributed, Autonomous and Learning Agents
✔ AI Grows Up
✔ The AI Revolution
✔ AI and Society
✔ Does AI Destroy Jobs?
✔ AI and Transportation
✔ Service Robotics
✔ Agents
✔ Knowledge-Based Systems
● Propositional Logic
✔ Syntax
✔ Semantics
✔ Proof Systems
✔ Resolution
✔ Horn Clauses
✔ Computability and Complexity
✔ Applications and Limitations
● First-order Predicate Logic
✔ Syntax
✔ Semantics
✔ Equality
✔ Quantifiers and Normal Forms
✔ Proof Calculi
✔ Resolution
✔ Resolution Strategies
✔ Equality
✔ Automated Theorem Provers
✔ Mathematical Examples
✔ Applications
● Limitations of Logic
✔ The Search Space Problem
✔ Decidability and Incompleteness
✔ The Flying Penguin
✔ Modeling Uncertainty
● Logic Programming with PROLOG
✔ PROLOG Systems and Implementations
✔ Simple Examples
✔ Execution Control and Procedural Elements
✔ Lists
✔ Self-modifying Programs
✔ A Planning Example
✔ Constraint Logic Programming
● Search, Games and Problem Solving
✔ Introduction
✔ Uninformed Search
✔ Breadth-First Search
✔ Depth-First Search
✔ Iterative Deepening
✔ Comparison
✔ Cycle Check
✔ Heuristic Search
✔ Greedy Search
✔ A-Search
✔ Route Planning with the A Search Algorithm
✔ IDA★-Search
✔ Empirical Comparison of the Search Algorithms
✔ Games with Opponents
✔ Minimax Search
✔ Alpha-Beta-Pruning
✔ Non-deterministic Games
✔ Heuristic Evaluation Functions
✔ Learning of Heuristics
✔ State of the Art
✔ Chess
✔ Go
● Reasoning with Uncertainty
✔ Computing with Probabilities
✔ Conditional Probability
✔ The Principle of Maximum Entropy
✔ An Inference Rule for Probabilities
✔ Maximum Entropy Without Explicit Constraints
✔ Conditional Probability Versus Material Implication
✔ MaxEnt-Systems
✔ The Tweety Example
✔ LEXMED, an Expert System for Diagnosing Appendicitis
✔ Appendicitis Diagnosis with Formal Methods
✔ Hybrid Probabilistic Knowledge Base
✔ Application of LEXMED
✔ Function of LEXMED
✔ Risk Management Using the Cost Matrix
✔ Performance
✔ Application Areas and Experiences
✔ Reasoning with Bayesian Networks
✔ Independent Variables
✔ Graphical Representation of Knowledge as a Bayesian Network
✔ Conditional Independence
✔ Practical Application
✔ Software for Bayesian Networks
✔ Development of Bayesian Networks
✔ Semantics of Bayesian Networks
● Machine Learning and Data Mining
✔ Data Analysis
✔ The Perceptron, a Linear Classifier
✔ The Learning Rule
✔ Optimization and Outlook
✔ The Nearest Neighbor Method
✔ Two Classes, Many Classes, Approximation
✔ Distance Is Relevant
✔ Computation Times
✔ Summary and Outlook
✔ Case-Based Reasoning
✔ Decision Tree Learning
✔ A Simple Example
✔ Entropy as a Metric for Information Content
✔ Information Gain
✔ Application of C4 5
✔ Learning of Appendicitis Diagnosis
✔ Continuous Attributes
✔ Pruning—Cutting the Tree
✔ Missing Values
✔ Cross-Validation and Overfitting
✔ Learning of Bayesian Networks
✔ Learning the Network Structure
✔ The Naive Bayes Classifier
✔ Text Classification with Naive Bayes
✔ One-Class Learning
✔ Nearest Neighbor Data Description
✔ Clustering
✔ Distance Metrics
✔ k-Means and the EM Algorithm
✔ Hierarchical Clustering
✔ How is the Number of Clusters Determined?
✔ Data Mining in Practice
✔ The Data Mining Tool KNIME
● Neural Networks
✔ From Biology to Simulation
✔ The Mathematical Model
✔ Hopfield Networks
✔ Application to a Pattern Recognition Example
✔ Analysis
✔ Summary and Outlook
✔ Neural Associative Memory
✔ Correlation Matrix Memory
✔ The Binary Hebb Rule
✔ A Spelling Correction Program
✔ Linear Networks with Minimal Errors
✔ Least Squares Method
✔ Application to the Appendicitis Data
✔ The Delta Rule
✔ Comparison to the Perceptron
✔ The Backpropagation Algorithm
✔ NETtalk: A Network Learns to Speak
✔ Learning of Heuristics for Theorem Provers
✔ Problems and Improvements
✔ Support Vector Machines
✔ Deep Learning
✔ Nature as Example
✔ Stacked Denoising Autoencoder
✔ Other Methods
✔ Systems and Implementations
✔ Applications of Deep Learning
✔ Creativity
✔ Applications of Neural Networks
● Reinforcement Learning
✔ Introduction
✔ The Task
✔ Uninformed Combinatorial Search
✔ Value Iteration and Dynamic Programming
✔ A Learning Walking Robot and Its Simulation
✔ Q-Learning
✔ Q-Learning in a Nondeterministic Environment
✔ Exploration and Exploitation
✔ Approximation, Generalization and Convergence
✔ Applications
✔ AlphaGo, the Breakthrough in Go
✔ Curse of Dimensionality
Table of content
● Introduction
✔ Definitions and background
✔ Organization of this monograph
● Some Historical Context of Deep Learning
● Three Classes of Deep Learning Networks
✔ A three-way categorization
✔ Deep networks for unsupervised or generative learning
✔ Deep networks for supervised learning
✔ Hybrid deep networks
● Deep Autoencoders — Unsupervised Learning
✔ Introduction
✔ Use of deep autoencoders to extract speech features
✔ Stacked denoising autoencoders
✔ Transforming autoencoders
● Pre-Trained Deep Neural Networks — A Hybrid
✔ Restricted Boltzmann machines
✔ Unsupervised layer-wise pre-training
✔ Interfacing DNNs with HMMs
● Deep Stacking Networks and Variants Supervised Learning
✔ Introduction
✔ A basic architecture of the deep stacking network
✔ A method for learning the DSN weights
✔ The tensor deep stacking network
✔ The Kernelized deep stacking network
● Selected Applications in Speech and Audio Processing
✔ Acoustic modeling for speech recognition
✔ Speech synthesis
✔ Audio and music processing
● Selected Applications in Language Modeling and Natural Language Processing
✔ Language modeling
✔ Natural language processing
● Selected Applications in Information Retrieval
✔ A brief introduction to information retrieval
✔ SHDA for document indexing and retrieval
✔ DSSM for document retrieval
✔ Use of deep stacking networks for information retrieval
● Selected Applications in Object Recognition and Computer Vision
✔ Unsupervised or generative feature learning
✔ Supervised feature learning and classification
● Selected Applications in Multimodal and Multi-task Learning
✔ Multi-modalities: Text and image
✔ Multi-modalities: Speech and image
✔ Multi-task learning within the speech, NLP or image
Table of content
● Getting Started with Machine Learning
Introduction to Machine Learning What is Machine Learning? Why Machine Learning Matters Types of Machine Learning Learning with Examples (Supervised) Discovering Patterns (Unsupervised) Learning from Experience (Reinforcement)● Your First Steps in Machine Learning
Understanding Data What is Data in Machine Learning? Cleaning and Preparing Data Basic Algorithms Predicting with Linear Regression Making Decisions with Decision Trees Grouping with Clustering● Diving Deeper into Machine Learning
Improving Predictions Support Vector Machines Learning Nearest Neighbors The Power of Ensembles Combining Predictions Random Forests Introduction to Neural Networks What are Neural Networks? Training Neural Networks● Solving Real-World Problems
Working with Text (Natural Language Processing) Understanding and Analyzing Text Making Sense of Sentiments Making Sense of Images (Computer Vision) Identifying Objects Understanding Images● Improving Your Machine Learning Skills
Evaluating and Tuning Models How Well is Your Model Doing? Making Your Model Better● Looking Ahead in Machine Learning
Challenges and Considerations Being Ethical in Machine Learning What’s Next in Machine Learning? Still Have Questions?
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