Education

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 is the final course for the Applied Data Analytics Certificate (ADAC) from BCIT Computing and assumes students have completed both MATH 3060 and COMP 2854 prior to this course. Building on statistics, data mining, Python for Data Analysis and visualization techniques, students who have completed the core requirements of ADAC will be able to integrate these advanced topics into a data analytics project. Participants are introduced to emerging sources of digital mapping data, data standards, mapping compilation procedures, mapping systems, plus an introduction to cartography tools and techniques in data analytics and Big Data. Labs and exercises use Excel, Python and Apache Spark for data mining with a focus on Business Intelligence and Big Data. Discussions also include NoSQL databases, and Hadoop. Students learn to work with a variety of structured and unstructured data sources to model, analyze and visualize data. Upon successful completion, participants will be able to present an advanced data analytics project start-to-finish. Prerequisite(s)
COMP 2256 and COMP 2454 and COMP 2854 and COMP 3840 and MATH 3060

Artificial Intelligence (AI) is the intelligence expected to be demonstrated by machines and computer programs. This course is designed to provide students with expertise in creating and modifying required AI algorithms and techniques. The first part of the course will focus on classic AI solutions (especially decision making) while the second part will cover some of machine learning (ML) related applications of AI (with an emphasis on learning from examples). The course will consider real world problems that need to be solved with applications of AI and the techniques used to build such applications (e.g. using the techniques to create challenging non player characters (NPC) in games development or password strength classification and intrusion detection in network security). More specifically, students will learn about the searching paradigm in designing intelligent agents and will practice implementing search algorithms. Logical knowledge representation and reasoning (another essential tool for AI experts) will be another topic in this course. Probabilistic reasoning will also be explored to help students learn how to deal with incomplete information and uncertainty. The course will also examine different learning techniques to guide students in creating self-learning models that can improve performance in decision-making over time through practical examples.
Prerequisite(s) COMP 8042

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

Machine Learning—it’s not just a buzzword any more.
A subset of artificial intelligence, Machine Learning (ML) is the science of getting computers to uncover key connections and make decisions without being explicitly programmed. It has given us many striking new applications, like self-driving cars and speech recognition.
This program introduces the tools, techniques, opportunities and applications of ML from a strategic perspective. Unlock the opportunities of this powerful technology to create greater value and competitive edge in your business.

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.

This course examines the tools, techniques, and strategies to analyze and manage data within an organization’s marketing and customer-related functions. It looks at the evolution, impact, and importance of data-driven decision-making in today’s organizations, as well as the changing role of marketers, customer service managers, and business / product leaders in how they use data to work with internal and external stakeholders.
This foundational course prepares participants for the next two required courses of the Associate Certificate in Data and Marketing Analytics program by introducing key terminology and concepts addressed in later more detailed courses of this program.

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.

This course examines the tools, techniques, and strategies required to collect, manage, and analyze customer data. Specifically, participants will look at the role of data in understanding, driving, and improving customer acquisition, customer support, and customer retention. Leveraging a variety of customer touch-points, the course will look at customer segmentation based on customer needs, customer life cycle, and customer experience. Once completed, participants will be able to use customer analytics to better understand their customers’ behaviors and to make effective and profitable customer decisions.
This course is recommended as a precursor to the Data Visualization and Storytelling course in the Associate Certificate in Data and Marketing Analytics program.

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, three-day workshop offers foundational data science and AI concepts. 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.

This workshop runs from February 17-19, 9am-12pm PST each day.

Artificial intelligence has the potential to reshape virtually every industry, the workforce and, ultimately, the entire economy. The rapid evolution of AI brings new buzzwords – machine learning, deep learning, neural networks – and many questions about how best to understand and implement these new technologies.
This half-day workshop is designed to build your understanding of AI – its terms, tools and solutions - to put you at the forefront of helping lead this change.
Dr. Bethany Edmunds, Director of Computer Science at Northeastern University’s Vancouver Campus, will help you understand what AI is, the unique potential it offers while examining why it creates significant fear of change for many in our workforce. You'll also gain a better understanding of the importance of having members of diverse backgrounds to shape AI strategies and solutions to ensure a more inclusive future for all of us.
Prior knowledge and experience in AI or Computer Science are not required to be successful in this workshop.

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.

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.

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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