BCT3209 Artificial Intelligence

The course introduces the fundamental problems, theories, and algorithms of the artificial intelligence field. The course also exposes students to applications of robotics.

Course Objectives

• Formulate search problems and implement search algorithms using admissible heuristics
• Formulate constraint satisfaction problems and find solutions using constraint graphs
• Describe games as adversarial search problems and implement optimal and efficient solutions
• Formulate nondeterministic search as Markov decision processes and solve the Bellman equations in reinforcement learning contexts
• Formulate Bayes' nets for stochastic problems and use them to solve inference problems
• Solve temporal applications using hidden Markov models and filtering algorithms
• Define the machine learning problem and implement simple algorithms including Naive Bayes, perceptrons, and clustering.

Learning outcomes

The student will be able to:

• Explain the properties of intelligent behavior
• Explain the central goals of intelligent behavior
• Explain the possibilities and the limitations of AI
• Use prepositional logic and first order logic to formulate AI problems and approximate human reasoning 
• Use informed search methods in problems solving
• Explain the principles of machine learning and how it compares to human learning 
• Explain the principles of robotics