Popular
AI Artificial Intelligence
Product Code:
AI2024
Product Condition:
New
$1,000.00 inc. tax
Description
Course Perspective
This course introduces students to the basic knowledge : representation, problem solving, and learning methods of artificial intelligence. Upon completion, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.
Course Content
This course introduces students to the basic knowledge : representation, problem solving, and learning methods of artificial intelligence. Upon completion, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.
| Theory | 1 Hour |
| Practice | 1 Hour |
Course Content
Week 1
Topic: Introduction To Artificial Intelligence
Topic: Introduction To Artificial Intelligence
Detail: 1 Introduction 1 1.1 What Is AI? 1.2 The Foundations of Artificial Intelligence 1.3 The History of Artificial Intelligence 1.4 The State of the Art
Week 2
Topic: Intelligent Agents
Topic: Intelligent Agents
Detail: 2.1 Agents and Environments 2.2 Good Behavior: The Concept of Rationality 2.3 The Nature of Environments 2.4 The Structure of Agents
Week 3
Topic: Problem-solving
Topic: Problem-solving
Detail: 3 Solving Problems by Searching 64 3.1 Problem-Solving Agents 3.2 Example Problems 3.3 Searching for Solutions 3.4 Uninformed Search Strategies 3.5 Informed (Heuristic) Search Strategies 3.6 Heuristic Functions
Week 4
Topic: Beyond Classical Search
Topic: Beyond Classical Search
Detail: 4.1 Local Search Algorithms and Optimization Problems 4.2 Local Search in Continuous Spaces 4.3 Searching with Nondeterministic Actions 4.4 Searching with Partial Observations 4.5 Online Search Agents and Unknown Environments . . . .
Week 5
Topic: Constraint Satisfaction Problems
Topic: Constraint Satisfaction Problems
Detail: 6.1 Defining Constraint Satisfaction Problems 6.2 Constraint Propagation: Inference in CSPs 6.3 Backtracking Search for CSPs 6.4 Local Search for CSPs 6.5 The Structure of Problems
Week 6
Topic: Making Simple Decisions
Topic: Making Simple Decisions
Detail: 16.1 Combining Beliefs and Desires under Uncertainty 16.2 The Basis of Utility Theory 16.3 Utility Functions 16.4 Multiattribute Utility Functions 16.5 Decision Networks 16.6 The Value of Information 16.7 Decision-Theoretic Expert Systems
Week 7
Topic: Learning
Topic: Learning
Detail: 18 Learning from Examples 18.1 Forms of Learning 18.2 Supervised Learning 18.3 Learning Decision Trees 18.4 Evaluating and Choosing the Best Hypothesis 18.5 The Theory of Learning 18.6 Regression and Classification with Linear Models 18.7 Artificial Neural Networks 18.8 Nonparametric Models 18.9 Support Vector Machines . . . . . . . . . . . . . . . . . . . . 744 18.10 Ensemble Learning . . . . . Practical Machine Learning . . . . . . . . .
Week 8
Topic: Robotics
Topic: Robotics
Detail: 25.1 Introduction 25.2 Robot Hardware 25.3 Robotic Perception 25.4 Planning to Move 25.5 Planning Uncertain Movements 25.6 Moving 25.7 Robotic Software Architectures 25.8 Application Domains
Week 8
Topic: Foundation of AI
Topic: Foundation of AI
Detail: 26 Philosophical Foundations 1020 26.1 Weak AI: Can Machines Act Intelligently? 26.2 Strong AI: Can Machines Really Think? 26.3 The Ethics and Risks of Developing Artificial Intelligence
Week 9
Topic: The Present and Future
Topic: The Present and Future
Detail: 27.1 Agent Components 27.2 Agent Architectures 27.3 Are We Going in the Right Direction? 27.4 What If AI Does Succeed? A Mathematical background 1053 A.1 Complexity Analysis and O() Notation A.2 Vectors, Matrices, and Linear Algebra A.3 Probability Distributions B Notes on Languages and Algorithms 1060 B.1 Defining Languages with Backus–Naur Form (BNF) B.2 Describing Algorithms with Pseudocode B.3 Artificial Intelligence and the Information Sciences
Week 10
Topic: Robotics
Topic: Robotics
Detail: 25.1 Introduction 25.2 Robot Hardware 25.3 Robotic Perception 25.4 Planning to Move 25.5 Planning Uncertain Movements 25.6 Moving 25.7 Robotic Software Architectures 25.8 Application Domains
Week 11
Topic: POPULAR SEARCH ALGORITHMS
Topic: POPULAR SEARCH ALGORITHMS
Detail: Single Agent Path-finding Problems Search Terminology Brute-Force Search Strategies Informed (Heuristic) Search Strategies Local Search Algorithms
Week 12
Topic: EXPERT SYSTEMS
Topic: EXPERT SYSTEMS
Detail: What are Expert Systems? Capabilities of Expert Systems Components of Expert Systems Knowledge Base Inference Engine User Interface Expert Systems Limitations Applications of Expert System Expert System Technology Development of Expert Systems: General Steps Benefits of Expert Systems.
Updating Order Details
Please do not refresh or navigate away from the page!
Recently Viewed
