Artificial intelligence (AI) is a research field that studies how to realize the intelligent human behaviors on a computer. The ultimate goal of AI is to make a computer that can learn, plan, and solve problems autonomously. Although AI has been studied for more than half a century, we still cannot make a computer that is as intelligent as a human in all aspects. However, we do have many successful applications. In some cases, the computer equipped with AI technology can be even more intelligent than us. The Deep Blue system which defeated the world chess champion is a well-know example.

The main research topics in AI include: problem solving, reasoning, planning, natural language understanding, computer vision, automatic programming, machine learning, and so on. Of course, these topics are closely related with each other. For example, the knowledge acquired through learning can be used both for problem solving and for reasoning. In fact, the skill for problem solving itself should be acquired through learning. Also, methods for problem solving are useful both for reasoning and planning. Further, both natural language understanding and computer vision can be solved using methods developed in the field of pattern recognition.

In this course, we will study the most fundamental knowledge for understanding AI. We will introduce some basic search algorithms for problem solving; knowledge representation and reasoning; pattern recognition; fuzzy logic; and neural networks.

The main purpose of this course is to provide the most fundamental knowledge to the students so that they can understand what the AI is. Due to limited time, we will try to eliminate theoretic proofs and formal notations as far as possible, so that the students can get the full picture of AI easily. Students who become interested in AI may go on to the graduate school for further study.

Artificial intelligence has become a powerful driving force in a wide range of industries, helping people and businesses create exciting, innovative products and services, enable more informed business decisions, and achieve key performance goals. The median salary of an AI engineer in the US is $171,715(Source: Datamation). By 2022, the AI market will grow at a CAGR of 53.25 per cent, and an estimated. 2.3 million jobs will be created in the AI field by 2020


  • What is Artificial Intelligence?
  • Philosophy of AI
  • Goals of AI
  • What Contributes to AI?
  • Programming Without and With AI
  • What is AI Technique?
  • Applications of AI
  • History of AI


  • What is Intelligence?
  • Types of Intelligence
  • What is Intelligence Composed of?
  • Difference between Human and Machine Intelligence


  • Real Life Applications of Research Areas
  • Task Classification of AI
  • Artificial Intelligence


  • What are Agent and Environment?
  • Agents Terminology
  • Rationality
  • What is Ideal Rational Agent?
  • The Structure of Intelligent Agents
  • The Nature of Environments
  • Properties of Environment


  • Single Agent Pathfinding Problems
  • Search Terminology
  • Brute-Force Search Strategies
  • Informed (Heuristic) Search Strategies
  • Local Search Algorithms


  • What is Fuzzy Logic?
  • Why Fuzzy Logic?
  • Fuzzy Logic Systems Architecture
  • Example of a Fuzzy Logic System
  • Application Areas of Fuzzy Logic
  • Advantages of FLSs
  • Disadvantages of FLSs
  • Artificial Intelligence


  • Components of NLP
  • Difficulties in NLU
  • NLP Terminology
  • Steps in NLP
  • Implementation Aspects of Syntactic Analysis


  • 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


  • What are Robots?
  • What is Robotics?
  • Difference in Robot System and Other AI Program
  • Robot Locomotion
  • Components of a Robot
  • Artificial Intelligence
  • Computer Vision
  • Tasks of Computer Vision
  • Application Domains of Computer Vision
  • Applications of Robotics


  • What are Artificial Neural Networks (ANNs)?
  • Basic Structure of ANNs
  • Types of Artificial Neural Networks
  • Working of ANNs
  • Machine Learning in ANNs
  • Bayesian Networks (BN)
  • Applications of Neural Networks