This hands-on entry level course provides an overview of Artificial Intelligence (AI) and is offered by BCIT Computing Part-time Studies. There are no specific prerequisites other than being proficient in using a personal computer, and being able to interact and communicate with others on a group project. Aimed at business or IT professionals and other who are curious about learning AI fundamentals, students begin by discussing AI concepts, and making comparisons to Machine Learning (ML) and Data Science. Lectures combined with labs and exercises focus using AI solutions. COMP 1021 was developed as part Canada's Digital Technology Supercluster capacity building project Athena Pathways. It is designed to help more Canadian women see the potential of the tech sector, and how a career in AI aligns with their skills and interests. Topics include; supervised learning, neural networks, unsupervised learning, and reinforcement learning. Commonly used AI tools and platforms allow participants to prepare data, train and evaluate their models. Online discussions include: natural language processing and decision making, as well as social issues and implications due to the limitations of AI. COMP 1021 is an elective in the Applied Information Systems (ACIS) Associate Certificate, which is a sub-set of the Computer Systems Technology (CST) Diploma. Please Note: COMP 1021 only counts for credit in ACIS and is not available to international students. Evaluation includes significant participation, labs, activities, online quizzes, group work and a project presentation. Upon successful completion students will be able to develop and deploy a no-code AI solution using Microsoft Azure. This course is under review and not currently offered.
- 60% in COMP 1002
- Not offered this term
- This course is not offered this term. Please check back next term or subscribe to receive notifications of future course offerings and other opportunities to learn more about this course and related programs.
Upon successful compleltion of this course, the student will be able to:
- Define Artificial Intelligence (AI).
- Explain the similarities and differences between Machine Learning (ML) and Data Science workflows using real-world examples.
- Illustrate the inner workings of major AI techniques including techniques used in Natural Language Processing (NLP) and decision making.
- Compare structured and unstructured data and Describe machine learning techniques that work best for each type of data.
- Analyze complex AI products to discover and describe the underlying AI pipeline.
- Recognize commonly used AI tools and platforms.
- Build, test and deploy a simple AI Solution using Microsoft Azure Cognitive Services.
- Identify various roles & responsibilities within an AI team.
- Define a process for identifying feasible and valuable AI projects within a company.
- Explain limitations of AI and the important social issues arising from these limitations; including the issues of discrimination and bias.
Effective as of Fall 2020
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