Education

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.

This on-line course is designed to introduce you to the use, benefits and impact of IoT not only in industry and business but in our day-to-day lives. You will learn how data is communicated, collected, analyzed and abstracted, and delivered to the end user to make life safer, more efficient and to assist in achieving better data to make better decisions.
The IoT Application Framework will be introduced as a practical application development and deployment roadmap to assist the end user in successfully implementing IoT solutions. You will also learn some of the challenges inherent in setting up and using IoT effectively including cybersecurity concerns and privacy issues.

Special Topics in Artificial Intelligence: Machine Learning. Machine Learning is the study of computer algorithms that improve automatically through experience. It is one of the most exciting aspects of artificial intelligence, and is the basis for many of its industrial applications. It is the preferred framework for many applications, such as face detection, hand-written digit recognition, speech recognition, and credit card fraud detection. This course provides students with a grounding in both the theoretical justification for, and practical application of, machine learning algorithms. Covers techniques in supervised and unsupervised learning, the graphical model formalism, and algorithms for combining models. 

This course introduces Data Mining, an area that plays a key role in Big Data analytics. The goal of data mining is the efficient discovery of useful patterns in large datasets. This course focuses on fundamental data mining tasks and algorithms as well as key applications. It will prepare you both for developing your own data mining application and for starting your data mining research. Students taking this course are expected to have taken an algorithms course and to have an understanding of basic statistics equivalent to an entry-level course. The course project requires programming in Python or R, and students are expected to be proficient with one of these programming languages.

This course introduces fundamental concepts in robotics and related fields, including analytical methods for decision making, and machine learning in the context of robotics. Topics include modelling and simulation of robotic systems, optimization, optimal control, robotic safety, reinforcement learning, and robotic perception. Applications of the material include unmanned aerial vehicles and self-driving cars.

Models of algorithms for dimensionality reduction, nonlinear regression, classification, clustering and unsupervised learning; applications to computer graphics, computer games, bio-informatics, information retrieval, e-commerce, databases, computer vision and artificial intelligence.

Problem-solving and planning; state/action models and graph searching. Natural language understanding Computational vision. Applications of artificial intelligence.

This hands-on entry level course provides an overview of Artificial Intelligence (AI) and is offered by BCIT Computing Part-time Studies. 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.
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.
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.
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 provides an introduction to the various topics in artificial intelligence. Topics to be covered include search, games, constraint satisfaction problems, knowledge and reasoning, and learning. Students will develop an understanding of the basic concepts and algorithms used in artificial intelligence. Application of artificial intelligence to problems in different domains will also be discussed.Prerequisite: Completion of first year CST and Admission into the Artificial Intelligence and Machine Learning Option

Model fitting and prediction in the presence of correlation due to temporal and/or spatial association. ARIMA models.

Advanced machine learning methods in the context of natural language processing (NLP) applications. Bag of words, recommender systems, topic models, natural language as sequence data, Markov chains, and recurrent neural networks.

Today’s organizations have the ability to collect data across a wide variety of channels and activities. However, communicating critical take-aways to stakeholders and creating actionable insights and decisions is becoming increasingly difficult in this sea of data. In this course, participants will learn how to communicate and sell marketing strategies, activities and results using a combination of visual thinking, storytelling, dashboards, and data visualizations. Using industry-standard tools, participants will identify the appropriate charts, graphs, and visual elements required for effective data visualization and communication across a diverse range of audiences.
This course is recommended as the final required course in the Associate Certificate in Data and Marketing Analytics program. Following this course, participants can choose between Marketing Intelligence & Performance Optimization, Mobile and Social Media Analytics, or Agile Marketing for their final course.

The Align program enables a direct path to a Master’s in Computer Science for non-computer science majors and people without programming experience.

This part-time five-day course offers foundational Artificial Intelligence, Machine Learning (ML) and Data science concepts that are applicable in the humanities and social sciences. Throughout the course, you will examine best practices to critically evaluate and mitigate unwanted bias from sources such as data, algorithms or users. Through collaborative hands-on labs guided by SFU experts, you will come away with the kind of data science experience and knowledge that organizations and recruiters value.

This part-time, week-long course offers foundational data science and AI concepts that can be directly applied to your work and organization. Designed and taught by leading data science and AI experts at Simon Fraser University, this online interactive course offers a hands-on learning environment, enabling you to immediately apply concepts and techniques you learn with live feedback and guidance from instructors. You will come away with the kind of data science experience and knowledge that organizations and recruiters value.

New – This course introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them. The course is designed as a blended learning experience that combines instructor-led training with online materials on the Microsoft Learn platform (https://azure.com/learn). The hands-on exercises in the course are based on Learn modules, and students are encouraged to use the content on Learn as reference materials to reinforce what they learn in the class and to explore topics in more depth.

This one-day course will provide foundational level knowledge on cloud concepts; core Azure services; security, privacy, compliance, and trust; and Azure pricing and support.
This course is a high-level overview of Azure. It will provide foundational level knowledge of cloud services and how those services are provided with Microsoft Azure.

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Build a customer support chat Bot that uses artificial intelligence from the Microsoft Azure platform including language understanding and pre-built AI functionality in the Azure Cognitive Services.