Artificial Intelligent TMUL 3310

Course description:

This course on Artificial Intelligence (AI) covers the breadth of the field of AI, including topics such as searching, problem-solving, logic, knowledge representation, reasoning, learning, perception and AI languages. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.The Lab provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning; unsupervised learning; learning theory; reinforcement learning and adaptive control. 

Course Aims:

This course aims to enable the student to:
  • Learn the basic concepts and techniques of Artificial Intelligence.
  • Developing skills of using Artificial Intelligence algorithms for solving practical problems.
  • Understanding of both the achievements of AI and the theory underlying those achievements.
  • Have a basic proficiency in a traditional AI language including an ability to write simple to intermediate programs and an ability to understand code written in that language.
  • Understand  some of the most advanced topics of AI such as learning , natural language processing.

Course outcomes:

Upon completion of the course, student should be able to:
  • Know classical examples of artificial intelligence. 
  • Know characteristics of programs that can be considered "intelligent". 
  • Understand the use of heuristics in search problems and games. 
  • Know a variety of ways to represent and retrieve knowledge and information. 
  • Know the fundamentals of artificial intelligence programming techniques in a modern programming language. 
  • Consider ideas and issues associated with social technical, and ethical uses of machines that involve artificial intelligence.