Artificial Intelligence (AI): Principles and Techniques
Artificial Intelligence (AI) aims to make computers and information systems more “intelligent” to solve complex problems and provide more natural and effective services to human beings. AI has been a source of innovative ideas and techniques in computer science and has been widely applied to many information systems. This course provides a comprehensive, introduction to artificial intelligence, emphasizing advanced topics such as advanced search, reasoning and decision-making under uncertainty, and machine learning.
· Provide the attendees with comprehensive and in-depth knowledge of AI principles and techniques.
· Introducing AI’s fundamental problems, and the state-of-the-art models and algorithms used to undertake the problems.
Expose attendees to the frontiers of AI-intensive computing and information systems, while providing a sufficiently strong foundation to encourage further research
· Informed Search and Online Search
· Constraint Satisfaction Problems (CSP) and Distributed CSP
· Games and Adversarial Search
Knowledge, Reasoning, and Decision Making under Uncertainty
· Logic, Inference, and Ontology
· Automated Planning and Acting (Optional)
· Uncertainty, Graphical Models, and Probabilistic Reasoning
· Temporal Probabilistic Reasoning and Dynamic Bayesian Networks
· Complex Decision-Making
· Inductive and Analytic Learning
· Computational Learning Theory and Statistical Learning
· Reinforcement Learning, Deep Learning and Multiagent Learning
AI for Communication, Robotics, and Information Systems
· Natural Language Understanding and Statistical Language Processing
· Perception and Robotics (Optional)
· AI in Business Intelligence and E-Learning (Optional)