Course Overview
This course provides an introduction to the various topics in Artificial Intelligence (AI). Topics to be covered include search, games, regression, classification and neural networks. Students will develop an understanding of the fundamentals underpinning AI applications and gain hands-on experience through the development of AI systems. Practical work in course projects focuses on developing components of AI systems as well as analyzing real-world datasets from different domains. The societal implications and ethical considerations in the design of AI systems will also be discussed.
Prerequisite(s)
- Acceptance into the Bachelor of Technology in Computer Systems Degree program OR special permission (COMP2121, MATH3042, COMP3522 or equivalent).
Credits
3.0
- Retired
- This course has been retired and is no longer offered. Find other Flexible Learning courses that may interest you.
Learning Outcomes
Upon successful completion of this course, the student will be able to:
- Describe current applications of AI.
- Solve problems using uninformed, informed and adversarial search techniques.
- Explain the concepts of learning and the differences between unsupervised, supervised and reinforcement learning.
- Explain the basics of different regression, classification and neural network techniques.
- Apply machine learning techniques and algorithms to datasets.
- Perform model selection and parameter tuning of machine learning techniques.
- Design and develop components of AI systems.
- Identify potential applications of AI techniques to real-world problems.
- Investigate societal implications and ethics of AI.
Effective as of Spring/Summer 2020
Programs and courses are subject to change without notice. Find out more about BCIT course cancellations.