This course is an introductory course to artificial intelligence. The goal of this course is to provide students with the underlying principle of the artificial intelligence and soft computing paradigms with their advantages over traditional computing. Topics to be covered will include: Introduction to Intelligent Systems: Tools, Techniques and Applications; Expert Systems; Fuzzy Systems; Artificial Neural Networks; Genetic Algorithms; Case-based Reasoning; Data Mining; Intelligent Software Agents; Language Technology.
Intended learning outcomes
Knowledge &understand
Student enumerates the different approaches to artificial intelligence
Student learns the basics and history of artificial intelligence
Student enumerates and explains the different branches of artificial intelligence
Student explains the techniques and applications of artificial intelligence.
mental skills
Student compares approaches of artificial intelligence
Student distinguishes among (technologies / applications) of artificial intelligence
Student distinguishes among different types of branches of artificial intelligence
Student criticizes (technologies/branches) of artificial intelligence
Student distinguishes between (characteristics / features) of artificial intelligence.
Practical & professional skills
Student uses artificial intelligence software and tools
Student designs a machine learning model
General and transferable skills
Editorial communication and report writing
Team work
Commitment to performing exams and handing in assignments on time
Teaching and learning methods
Lectures
Mini-projects
Research papers
Methods of assessments
Midterm exam = 30
Final exam = 50
Scientific activities (eg writing a report or giving a presentation) = 20
Course contents
Introduction to artificial intelligence (definition/historical development/approaches)
