The applied artificial intelligence workshop. (2020)

• Record Type: Book
• Authors: So, Anthony; So, William; Nagy, Zsolt
• Contents: Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Artificial Intelligence -- Introduction -- How Does AI Solve Problems? -- Diversity of Disciplines in AI -- Fields and Applications of AI -- Simulation of Human Behavior -- Simulating Intelligence -- the Turing Test -- What Disciplines Do We Need to Pass the Turing Test? -- AI Tools and Learning Models -- Intelligent Agents -- The Role of Python in AI -- Why Is Python Dominant in Machine Learning, Data Science, and AI? -- Anaconda in Python -- Python Libraries for AI A Brief Introduction to the NumPy Library -- Exercise 1.01: Matrix Operations Using NumPy -- Python for Game AI -- Intelligent Agents in Games -- Breadth First Search and Depth First Search -- Breadth First Search -- Depth First Search (DFS) -- Exploring the State Space of a Game -- Estimating the Number of Possible States in a Tic-Tac-Toe Game -- Exercise 1.02: Creating an AI with Random Behavior for the Tic-Tac-Toe Game -- Activity 1.01: Generating All Possible Sequences of Steps in a Tic-Tac-Toe Game -- Exercise 1.03: Teaching the Agent to Win -- Defending the AI against Losses Activity 1.02: Teaching the Agent to Realize Situations When It Defends Against Losses -- Activity 1.03: Fixing the First and Second Moves of the AI to Make It Invincible -- Heuristics -- Uninformed and Informed Searches -- Creating Heuristics -- Admissible and Non-Admissible Heuristics -- Heuristic Evaluation -- Heuristic 1: Simple Evaluation of the Endgame -- Heuristic 2: Utility of a Move -- Exercise 1.04: Tic-Tac-Toe Static Evaluation with a Heuristic Function -- Using Heuristics for an Informed Search -- Types of Heuristics -- Pathfinding with the A* Algorithm Exercise 1.05: Finding the Shortest Path Using BFS -- Introducing the A* Algorithm -- A* Search in Practice Using the simpleai Library -- Game AI with the Minmax Algorithm and Alpha-Beta Pruning -- Search Algorithms for Turn-Based Multiplayer Games -- The Minmax Algorithm -- Optimizing the Minmax Algorithm with Alpha-Beta Pruning -- DRYing Up the Minmax Algorithm -- the NegaMax Algorithm -- Using the EasyAI Library -- Activity 1.04: Connect Four -- Summary -- Chapter 2: An Introductionto Regression -- Introduction -- Linear Regression with One Variable -- Types of Regression -- Features and Labels Feature Scaling -- Splitting Data into Training and Testing -- Fitting a Model on Data with scikit-learn -- Linear Regression Using NumPy Arrays -- Fitting a Model Using NumPy Polyfit -- Plotting the Results in Python -- Predicting Values with Linear Regression -- Exercise 2.01: Predicting the Student Capacity of an Elementary School -- Linear Regression with Multiple Variables -- Multiple Linear Regression -- The Process of Linear Regression -- Importing Data from Data Sources -- Loading Stock Prices with Yahoo Finance -- Exercise 2.02: Using Quandl to Load Stock Prices
• Publisher Details: Birmingham, UK : Packt Publishing
• Publication Date: 2020
• Extent: 1 online resource (1 volume), illustrations
• Subjects: 006.3; Artificial intelligence; Machine learning; Artificial intelligence; Machine learning; Electronic books; Electronic books
• Languages: English
• ISBNs: 9781800203730
• Related ISBNs: 180020373X; 9781800205819
• Notes: Note: Description based on online resource; title from title page (viewed October 22, 2020).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.