+251 111 559769      info@hilcoe.net
     +251 111 559769      info@hilcoe.net

Artificial Intelligence

Course Descriptions:

The field of artificial intelligence includes two distinct foci: the understanding of human intelligence and the development of computer programs to solve problems in non-traditional ways. The first of these emphasizes theoretical models, while the second draws upon various problem-solving strategies, algorithms, and data representations to solve specific problems. While the component of artificial intelligence that falls within computer science touches upon some theoretical models of human intelligence and behavior, more attention is paid to problem-solving strategies and the building of systems. This course provides a framework for considering multiple approaches for storing and processing symbolic data. The course builds upon concepts and algorithms from previous CS courses, developing alternative perspectives of topics studied earlier and introducing new approaches. Within the course, students will apply general principles and techniques to solve sample problems. Topics covered include: definitions of the term artificial intelligence; task environments; types of intelligent agents; theory underlying artificial intelligence including prepositional and predicate logic, probability theory, and searching, planning, reasoning with uncertainty and learning, robotics, some of the philosophical and ethical issues arising from the field of artificial intelligence, programming a significant artificial intelligence problem.


Upon completion of the course students will be able to

  1. Describe the key components of the artificial intelligence (AI) field
  2. Describe search strategies and solve problems by applying a suitable search method
  3. Describe mini-max search and alpha-beta pruning in game playing.
  4. Describe and apply knowledge representation
  5. Describe and list the key aspects of planning
  6. Describe and apply probability theorem and Bayesian networks.
  7. Describe the key aspects of intelligent agents.
  8. Describe the key aspects of Evolutionary computation, including genetic algorithms and genetic programming.
  9. Describe the key aspects of Machine learning

Course Content:

  • Introduction to AI
  • Basics of Search
  • Knowledge Representation and Reasoning
  • Planning
  • Bayesians Networks
  • Intelligent Agents
  • Evolutionary Computation
  • Machine Learning