COMP 3711: Artificial Intelligence
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.
Learning outcomes
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.
Note: Knowledge of a programming language, such as Java, will be helpful.
Course topics
- 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
The following materials are required for this course:
- Russell, S. & Norvig, P. (2021). Artificial intelligence: A modern approach (4th
ed.). Upper Saddle River, NJ: Pearson.
Type: Textbook. ISBN: 9780134610993
Students will need to access the following online resource 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
Optional materials
Professional Organizations and Publications
For their own professional development, students may want to follow and/or subscribe to the following two professional networks.
- IEEE Computational Intelligence Society
- IEEE Transactions on Computational Intelligence and AI in Games (Journal)
Demonstration Tools
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 OL Materials.
Assessments
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 exams@tru.ca 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 | 5% |
| Assignment 2: A* Search | 8% |
| Assignment 3: Beyond Classical Search | 10% |
| Assignment 4: Problematic Reasoning and Knowledge Representation | 15% |
| Assignment 5: Machine Learning | 12% |
| Final Exam (mandatory) | 50% |
| Total | 100% |
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.
