AI and AI Systems

Curricula AI and AI Systems
Day 1: Foundations of Artificial Intelligence

Session 1: Introduction to AI

·       What is Artificial Intelligence?

·       Philosophy of AI and its goals.

·       Contributions to AI and programming without and with AI.

·       AI techniques and applications.

Session 2: Intelligent Systems

·       Understanding intelligence.

·       Types and composition of intelligence.

·       Comparing human and machine intelligence.

·       Real-world applications of intelligent systems.

Session 3: Research Areas of AI

·       Overview of AI research areas.

·       Classification of AI tasks.

·       Real-life applications of research areas.

Day 2: Agents, Environments, and Search Algorithms

Session 1: Agents and Environments

·       Defining agents and environments.

·       Terminology and rationality of agents.

·       Ideal rational agents and the structure of intelligent agents.

·       Properties of environments.

Session 2: Popular Search Algorithms

·       Single-agent pathfinding problems.

·       Terminology and brute-force search strategies.

·       Informed (heuristic) search strategies.

·       Local search algorithms.

Session 3: Fuzzy Logic Systems

·       Introduction to fuzzy logic.

·       Architecture of fuzzy logic systems.

·       Applications, advantages, and disadvantages of FLSs.

Day 3: Natural Language Processing, Expert Systems, Robotics, and Neural Networks

Session 1: Natural Language Processing (NLP)

·       Components and difficulties in NLP.

·       Terminology and steps in NLP.

·       Implementation aspects of syntactic analysis.

Session 2: Expert Systems

·       Definition and capabilities of expert systems.

·       Components, knowledge base, and inference engine.

·       User interface and limitations of expert systems.

·       Applications and technology in expert systems.

Session 3: Robotics and Neural Networks

·       Understanding robots and robotics.

·       Components of a robot and computer vision.

·       Application domains of computer vision.

·       Introduction to artificial neural networks (ANNs) and their basic structure.

The course provides a comprehensive overview of artificial intelligence, covering foundational concepts, intelligent systems, research areas, agents, environments, search algorithms, fuzzy logic systems, natural language processing, expert systems, robotics, and neural networks over three days.