Education

This course emphasizes support vector machine (SVM) algorithms and neural networks to improve predictive accuracy, reduce bias and lower variance in real world applications. Case studies compare the advantages and disadvantages of machine learning and neural network models with traditional regression models. The iterative nature of this course provides review and reinforcement of common practices of the predictive modeling cycle.

This course examines the theoretical and applied problems of constructing and modelling systems, which aim to extract and represent the meaning of natural language sentences or of whole discourses, but drawing on contributions from the fields of linguistics, cognitive psychology, artificial intelligence and computing science. This course is an introduction to NLP and will cover algorithms and techniques for processing text (using probabilistic models and neural networks) as well as basic linguistic concepts.

Computer vision is the process of automatically extracting information from images and videos. The course covers various aspects of Computer Vision, for example, imaging geometry (camera calibration, stereo, and panoramic image stitching), video analysis (motion detection and tracking), image segmentation, object recognition, and more. The course teaches both traditional techniques and more recent learning-based approaches such as deep neural networks, while we will focus increasingly more on the latter. The course will be based on lectures and assignments (Python and Matlab). Computational approaches to image understanding will be discussed in relation to theories about the operation of the human visual system and with respect to practical applications in robotics. Topics will include edge detection, shape from shading, stereopsis, optical flow, Fourier methods, gradient space, three-dimensional object representation and constraint satisfaction.

Provides a unified discussion of the fundamental approaches to the problems in artificial intelligence. The topics considered are: representational typology and search methods; game playing, heuristic programming; pattern recognition and classification; theorem-proving; question-answering systems; natural language understanding; computer vision.

Basic concepts and programming tools for handling and processing data. Includes data acquisition, cleaning data sources, application of machine learning techniques and data analysis techniques, large-scale computation on a computing cluster.

Principles and techniques underlying the design, implementation and evaluation of intelligent computational systems. Applications of artificial intelligence to natural language understanding, image understanding and computer-based expert and advisor systems. Advanced symbolic programming methodology.

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

This course introduces machine learning methods for the life-sciences, focusing on molecular-level data, in particular genomic data. Such data plays a crucial role in precision medicine, e.g. drug response prediction, and in public health, e.g. the tracking of infectious diseases. However, genomic data poses special challenges to machine learning, due to the small number of examples (e.g. patients with clinical information) and great complexity of every example (e.g., SNP, CNV, RNA-seq, omics). The instructors will start the course with a few tutorial-style introductions of foundations. Students will prepare and give presentations on a state-of-the-art research paper. Students will, in small groups, perform a course research project in which they reproduce and extend the results of a recent paper from one of the four given focus areas (see the Topics below). In the last phase of the course, students will present the results of their projects. General guidelines and strategies for writing clearly and giving good talks will be given, and students will receive constructive feedback on their presentations and project reports from the instructor and other students.

Graph data represent relationships between entities in a domain. They are a common data type, which makes them important for many applications. Domains with major graph datasets include the following: enterprise data management through relational databases, social networks, bioinformatics (e.g. protein-protein interactions), information extraction in natural language processing, where knowledge graphs represent a large amount of information that can be extended through on-line sources. While graph data are powerful and widely available, they are a challenge for standard machine learning methods that are designed for independent data points. The goal of this course is to introduce students to the special challenges of learning from graph data, and to the machine learning methods that have been developed to address them. Many recent approaches are based on deep learning, because neural methods provide accurate predictions and are relatively easy to implement. The course will therefore emphasize graph neural networks. The course is an in-person seminar course, which means that I expect strong participation from students. I will give lectures to fill in background but as much as possible classes will be interactive. About half the class will be devoted to background on previous machine learning methods for graph data, and about half to discussing advanced topics, current research papers, and project ideas.

Aligned with the ACMP Standard for Change Management®, this course examines the tools and techniques used to develop and manage the change process and looks at the impact change management has on an organization, from human resources and operations, to IT and finance. Gain a greater understanding of a change management practitioner’s roles and responsibilities and how they work with internal and external stakeholders.
This foundation course prepares participants for the next three courses of the UBC Associate Certificate in Change Management by introducing key terminology and concepts addressed in the later more detailed courses of this program.

Data and Marketing Analytics Fundamentals provides you with an overarching framework covering Data, Analytics, and Visualization, as well as the fundamental principles, concepts, and terminology associated with data and marketing analytics. We delve deeper into analytics with a focus on those that relate to customer experiences, social engagement, and mobile practices. Not only will we highlight common analytics used, but also the platforms and typical touch points where this data is collected. We will also review how to interpret and analyze these analytics to improve your customer experiences, customer engagement, and overall customer satisfaction, allowing you to move from past analyses to a more predictive future using analytics. You will also learn how to communicate marketing strategies using dashboards and visualizations to get stakeholder buy in.

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

An introduction to machine learning concepts and vocabulary to facilitate ML and AI-based product design, specification, and development. Participants will engage in the entire product design life cycle, including problem specification, data collection and curation, selecting appropriate ML techniques, iterative development, specifying and evaluating performance, marketing considerations, and continuous improvement. Participants will also learn about identifying and addressing bias in data sets, evaluating customer consequences of false results, and the challenges of interpreting the outputs of ML/AI systems.

Designed and taught by leading data science and AI experts at Simon Fraser University, this training program enables emerging leaders from industry and government to develop data science and leadership skills. Through this outcome-focused experiential training program offered by SFU’s Big Data Hub, you will tackle a valuable business problem for your current employer or for a partner organization while gaining an introduction to all aspects of a data project workflow. You will come away with relevant knowledge, training, and a high-quality data project ready for field trial and implementation at the end of the program.

This introductory-level course is perfect for middle and upper managers, analysts, project leaders and anyone working in a non-technical role who has had limited exposure to business analytics but would like to gain a greater understanding of the concepts.

2-day interactive course to learn how to use data-driven approaches to promote productivity and growth.

Offered by Asper Executive Education in collaboration with UBC Sauder Executive Education

No matter who you are in the world of business, getting a big-picture view of how business really works can be a pivotal strategy for advancing your career and growing your organization. 

Spread over a concise five weeks, this robust program provides a comprehensive look at the fundamentals of business and current organizational practices. It covers all the key components of a formal business school curriculum, enabling you to increase your business acumen and bring fresh insights to your work without having to commit full time to study. 

Led by award-winning Sauder faculty, the program will expand your knowledge and applied skills in strategy, financial management, operations, strategic HR and marketing. You’ll also sample emerging areas in innovation, data analytics and project management. Finally, you’ll explore how decisions in one area may impact the others, and how strategic integration between functions can drive organizational success. 

For Leaders, managers and entrepreneurs with experience in leading teams, managing resources and implementing business strategies.  Cultivate an innovative organizational culture where digital can thrive by becoming an effective change agent. Learn how to pitch your vision, while carefully balancing resistance and influence during your conversations with internal and external stakeholders. Define your current vs. future state of the business, and build a digital maturity model that drives your organization forward. Develop digital talent that is high-performing, social, customer-centric, and entrepreneurial.

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.

Use the following link to see the current list of available dates. Once you have decided which dates work for you, please fill out the application form at the bottom of this page. Note that all times on their website are in EST and not PST.

Build AI infused applications that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. The course will use C# or Python as the programming language.

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.

Use the following link to see the current list of available dates. Once you have decided which dates work for you, please fill out the application form at the bottom of this page. Note that all times on their website are in EST and not PST.