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Machine Learning (ML), Artifical Intelligence (AI), and Big Data Analytics are an important topic for talent in Calgary and Alberta. Knowledge of these three subjects is critical for individuals across disciplines to understand because of its useful applications in business and its societal impacts. However, there is little existing work on teaching AI + ML to computing and non-computing students. The Mount Royal AI + ML workshop will allow students, alumni, and industry professionals from various STEM disciplines to get hands-on experience with AI + ML and to apply them in their fields.
A 2018 World Economic Forum report projected that by 2022, 73% of businesses will adopt machine learning technology into their business. The Mount Royal AI + ML workshop is dedicated to advancing the understanding and implementation of AI + Machine Learning by bringing together leading experts from academia and industry.
Prerequisites: Participants are expected to be familiar with a programming language. The workshops will be in Python and R languages.
Laptop software: Participants are expected to bring their own laptop. Mount Royal students and faculty can borrow laptops from Mount Royal Library.
Session 1 - Introduction to ML & DA
Using the Jupyter notebook environment, this session will introduce participants to the Python Data Science ecosystem. This includes libraries for data wrangling (numpy and pandas), visualization (matplotlib and plotly) and machine learning (scikit-learn). The material will be presented in a tutorial style using example datasets so that participants can follow along on their own laptops.
Software requirements: Jupyter notebook environment. Participants can use Google Colab.
Using the Jupyter notebook environment, this session will introduce participants to the Python Data Science ecosystem. This includes libraries for data wrangling (numpy and pandas), visualization (matplotlib and plotly) and machine learning (scikit-learn). The material will be presented in a tutorial style using example datasets so that participants can follow along on their own laptops.
Software requirements: Jupyter notebook environment. Participants can use Google Colab.
Session 4 - Introduction to Neural Networks
This session will introduce the foundations of neural networks and will allow the audience to understand the key parameters in a neural network’s architecture. During this session, students will build, train, and test a neural network using python.
Software requirements: Python, Jupyter notebook environment. Participants can use Google Colab.
This session will introduce the foundations of neural networks and will allow the audience to understand the key parameters in a neural network’s architecture. During this session, students will build, train, and test a neural network using python.
Software requirements: Python, Jupyter notebook environment. Participants can use Google Colab.
Session 2 - Regression
R Studio is an integral tool of the Data Science environment. This session will be an introduction to R Studio as a ‘lab book’ for data-science themed work, and will specifically include a review of structured learning and the testing of various machine learning models. This will be an interactive session where participants work with rich data sets on their own laptops.
Software requirements: Participants are strongly recommended to dowlnoad R Studio to their laptops. https://www.rstudio.com/products/rstudio/download/
R Studio is an integral tool of the Data Science environment. This session will be an introduction to R Studio as a ‘lab book’ for data-science themed work, and will specifically include a review of structured learning and the testing of various machine learning models. This will be an interactive session where participants work with rich data sets on their own laptops.
Software requirements: Participants are strongly recommended to dowlnoad R Studio to their laptops. https://www.rstudio.com/products/rstudio/download/
Session 5 - Deep Learning Libraries
In this session, I will introduce Tensorflow, currently the most popular library for computations involving neural networks. I will explain the essential ideas underlying tensorflow and its most important APIs. We will interact with these APIs both by hand as well as through the higher level Keras front-end. My hope is that the audience acquires sufficient skill to experiment independently with simple feedforward and convolutional neural networks in Tensorflow.
Software requirements: Python, Numpy, Tensorflow 2.0, Keras, Jupyter notebook environment. Participants can use Google Colab.
In this session, I will introduce Tensorflow, currently the most popular library for computations involving neural networks. I will explain the essential ideas underlying tensorflow and its most important APIs. We will interact with these APIs both by hand as well as through the higher level Keras front-end. My hope is that the audience acquires sufficient skill to experiment independently with simple feedforward and convolutional neural networks in Tensorflow.
Software requirements: Python, Numpy, Tensorflow 2.0, Keras, Jupyter notebook environment. Participants can use Google Colab.
Session 3 - Classification & Clustering
This session will provide a general overview of supervised (classification) vs unsupervised (clustering) learning. This will be an interactive session where participants will apply clustering and classification techniques on a sample data set. By the end of this session, participants will be able to tell which technique fits a particular problem and how to implement a python script to approach the problem.
Software requirements: Python, Jupyter notebook environment. Participants can use Google Colab.
This session will provide a general overview of supervised (classification) vs unsupervised (clustering) learning. This will be an interactive session where participants will apply clustering and classification techniques on a sample data set. By the end of this session, participants will be able to tell which technique fits a particular problem and how to implement a python script to approach the problem.
Software requirements: Python, Jupyter notebook environment. Participants can use Google Colab.
Session 6 - Image Processing
This workshop will cover image processing with python. More specifically, it will cover: what is an image?, What is digital image processing?, and the applications of image processing, such as Face Recognition & Object Detection.
Software requirements: Python, OpenCV, Tensorflow, Keras, Jupyter notebook environment. Participants can use Google Colab.
This workshop will cover image processing with python. More specifically, it will cover: what is an image?, What is digital image processing?, and the applications of image processing, such as Face Recognition & Object Detection.
Software requirements: Python, OpenCV, Tensorflow, Keras, Jupyter notebook environment. Participants can use Google Colab.
Keynote Speakers
Keynote Speakers
Susan Ibach
Susan Ibach
Business Development Manager at AI Gaming
Keynote Title: Passion + Data Science = Opportunity
Sep. 27, 2019
Presenter: Susan Ibach (aka HockeyGeekGirl)
Bio: Susan is a Business Development Manager at AI Gaming. She has been working with data for over 20 years. She started out at a consulting firm, then discovered a passion for teaching technology to others. She spent over 10 years teaching industry developers how to work with code and data. She spent the last 8 years working at Microsoft helping developers in Canada and around the world learn how to leverage the cloud for its computing power and services including AI and machine learning. She currently does business development for AI Gaming, a company that develops customized AI games to help developers learn new skills.
Abstract: Machine Learning and Artificial Intelligence are all over the news, but what does it take to be a data scientist and what can I do with those skills? Whether your passion is hockey or healthcare, social justice or forensics science, starting up a small business or doing research, data science is a tool that can help solve problems and guide business decisions. Come discover what it means and what it takes to enter the world of data science!
Niaz Tadayyon
Niaz Tadayyon
Canada Resources Applied Intelligence Lead at Accenture
Keynote title: Digital twin – the new face of innovation and the capabilities in Energy
Sep. 28, 2019
Presenter: Niaz Tadayyon
Bio: Niaz Tadayyon is Canada Applied Intelligence (aka advanced analytics) lead for resources at Accenture. She helps oil, gas, mining and utility clients in their digital and advanced analytics journey, and build a new practice in Calgary to design & implement successful innovative solutions. Previously, Niaz held several leadership roles such as Chief Analytics Officer, Sr. Solution Architect, Analytics Architect and Data warehouse lead in consulting and energy industry. She helps organizations to discover unique insights of their data to improve data-driven decisions making. She has wealth of end-to-end analytics implementation and diverse background with relevant platforms in large and complex data integrations, Advanced Analytics and Machine learning/AI solutions. In addition, she has a track record of taking on challenging projects and bringing them to successful completion and proven ability to build and manage high performance teams. She loves public speaking, and has presented at some public speaking meetups and events in Calgary.
She holds a bachelor’s in applied mathematics in Computer Science from Sharif University and Master of Computer Engineering (M.Eng) from Ecole Polytechnique de Montreal.
Abstract: As part of digital transformation, is a technology used to compare actual (real-time) with optimal values to identify problems and assets issues before failure occurs to prevent downtime and developing process improvements. Basically, it’s a digital replica of critical assets such as pumps, compressors, entire plant, pipelines, facilities, processes and peoples to have a better visibility into the entire operations and their intercorrelations.
The concept has been around for more than three decades as a discipline of engineering simulation. However, with technology advancements, we’re able to simulate and mimic the reactions of assets when there is any change in operation parameters, processes and constraints. Using digital twin, we can test and asses new ideas and processes before taking action and implementing in the oil field, therefore protecting the health of workers, ensuring compliance, reduce risk and making data-driven decisions.
James Stauch
James Stauch
Sep. 28, 2019
Keynote Title: In Search of the Altruithm: AI &the Future of Social Good
Presenter: James Stauch
Director, Institute for Community Prosperity, Mount Royal University
Bio: James Stauch is the Director of the Institute for Community Prosperity at MRU where he has led the creation of non-credit, co-curricular and credit-based and programs for students and practitioners in social innovation, community investment and the economics of social change. He also co-founded the Trico Changemakers Studio, an on-campus community co-working and social R&D space. Previously, he served as a philanthropy executive and consultant, including as a senior executive with the Walter & Duncan Gordon Foundation in Toronto. He has chaired four national and international networks of funders, is a leadership faculty member for the Conference Board of Canada’s Corporate Responsibility and Sustainability Institute, and a a regular contributor to the Future of Good and KCI Philanthropy Trends. He is the lead researcher / author of an annual scan of trends and issues, produced in partnership with the Calgary Foundation. He recently co-authored a Students’ Guide to Mapping a System, published by the Skoll Centre for Social Entrepreneurship at Oxford University, In Search of the Althruithm: AI and the Future of Social Good, The Loney Companion: 10 Steps to Starting a Social Enterprise in Canada, and a chapter on the role of business in the community, as part of a forthcoming textbook on the nonprofit sector in Canada.
Abstract: The careful combination of machine super-intelligence with human learning, as we are seeing in many social purpose uses and fields already, has significant potential to help us solve even our most complex social and environmental challenges. Peppered with many real-world examples of existing and in-development AI and socially-purposeful applications, this session will explore the potential impacts of AI on societal transformation, arguing that the development of AI must not merely be ethical, but also inclusive and common-good focused. An 'all hands on deck' approach will be required, given the existential, public policy and social implications involved.
Event Time & Location
Event Time & Location
Time: Sep. 27 - Sep. 28, 9:00 am- 5:00 pm
Location: EL1270 – Ideas Lounge, Mount Royal University
Register at: https://mruai2019.eventbrite.ca
Event contacts:
Yasaman Amannejad, Assistant Professor, Department of Mathematics and Computing - yamannejad@mtroyal.ca
Jenn MacDonald, Talent and Program Development Manager, Institute for Innovation and Entrepreneurship, jemacdonald@mtroyal.ca
Thank you to the event sponsors:
NSERC Chair for Women in Science and Engineering
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