Students investigate AI algorithms that are used in wide application areas. Students are
introduced to the use of classical artificial intelligence techniques and soft computing
techniques. Classical artificial intelligence techniques include knowledge representation,
heuristic algorithms, rule based systems, and probabilistic reasoning. Soft computing techniques
include fuzzy systems, neural networks, and genetic algorithms. Students learn the basic concepts
of machine learning and the difference between supervised and unsupervised learning. Students
apply machine learning algorithms to solve real-life problems.
Upon completion of this course, students will have a sound understanding of artificial
intelligence, models, methods, and applications. Students should be able to:
- Examine the major areas and challenges of AI.
- Distinguish problems that are amenable to solution by AI methods, and which AI methods may be
suited to solving a given problem.
- Formalize a given problem in the language/framework of different AI methods.
- Implement basic AI algorithms using a programming language.
- Apply basic AI knowledge and algorithms to solve problems.
- Utilize machine learning software tools to classify datasets and analyze the results.
- Module 1: Artificial Intelligence (AI) and Agents
- Module 2: Problem Solving by Search Module 3: Beyond Classical Search
- Module 4: Probabilistic Reasoning and Knowledge Representation
- Module 5: Machine Learning
Required text and materials
Students will receive the following:
- Russell, S. & Norvig, P. (2010). Artificial intelligence: A modern approach (3rd
ed.). Upper Saddle River, NJ: Pearson.
Type: Textbook ISBN: 978-13-604259-4
Students will access the following online for free:
- Poole, D. L., & Mackworth, A. K. (2017). Artificial intelligence: Foundations of
computational agents (2nd ed.). Cambridge, United Kingdom: Cambridge University Press.
Available for free at: http://artint.info/2e/html/ArtInt2e.html
Professional Organizations and Publications
For your own professional development, you may
want to follow and/or subscribe to the following two professional networks.
These tools are helpful for learning and exploring concepts in artificial intelligence. You
will find these helpful at various points throughout the course.
Note: If you have questions about course textbooks or other materials, email OLMaterials.
Please be aware that should your course have a final exam, you are responsible for the fee to the online proctoring service, ProctorU, or to the in-person approved Testing Centre. Please contact email@example.com with any questions about this.
To successfully complete this course, students must achieve a passing grade of 50% or higher on
the overall course, and 50% or higher on the final mandatory exam.
|Assignment 1: Environment Simulator
|Assignment 2: A* Search
|Assignment 3: Beyond Classical Search
|Assignment 4: Problematic Reasoning and Knowledge Representation
|Assignment 5: Machine Learning
|Final Exam (mandatory)
Open Learning Faculty Member Information
An Open Learning Faculty Member is available to assist students. Students will receive the necessary contact information at the start of the course.