UBC CTLT - AI In Teaching and Learning
The Centre for Teaching, Learning, and Technology (CTLT) at UBC offers programming and project facilitation support that serves the diverse needs and audiences of the University Community. As a centrally-provisioned service unit, CTLT offers a range of services that address needs of individuals, academic units and the University mission as a whole.
More specifically, they can support faculty and staff with the use of Generative Artificial Intelligence in delivery of their teaching and learning. They help educators move from concern, through understanding, to seeing the opportunities generative AI presents.
Their website provides:
Applied Statistics and Data Science Group (ASDa)
The Applied Statistics and Data Science Group (ASDa) in the UBC Department of Statistics provides assistance in the statistical formulation of research questions, the design of experiments and sampling plans for surveys, the choice and explanation of statistical methodology, statistical computing and graphics, statistical analysis, the interpretation of findings, and more. ASDa also plays an active role in continuing education on and off the UBC campus, giving seminars and workshops on statistical concepts and methodology to various departments, research groups and at teaching hospitals.
- Hire one of ASDa's statistical consultants to discuss your project
- One-hour free consultation for graduate students at UBC
- Learning R at UBC
- Introduction to statistical concepts webinars (schedule and registration)
- Free statistical help from students taking STAT 551 (advice only) and STAT 450 (analysis)
Previously recorded topics from ASDa and Graduate Pathways for Success Program from the Faculty of Graduate Studies (UBC CWL access):
- Exploratory Data Analysis
- Study Design and Data Collection Essentials
- Two Group Comparisons and ANOVA
- Correlation and Linear Regression
- Discrete Data: Counts and Distribution (Logistic and Poisson Regression)
- Mixed Effects Models
Previously recorded topics from ASDa and Vancouver Coastal Health, with focus on medical studies:
Data Science: A First Introduction Textbook
Data Science: A First Introduction is an open source textbook originally written for UBC's DSCI 100 - Introduction to Data Science course aimed to introduce undergraduate students to data science.
This book is structured so that learners spend the first four chapters learning how to use R programming language and Jupyter notebooks to load, wrangle/clean, and visualize data, while answering descriptive and exploratory data analysis questions. The remaining chapters illustrate how to solve four common problems in data science, which are useful for answering predictive and inferential data analysis questions:
- Predicting a class/category for a new observation/measurement
- Predicting a value for a new observation/measurement
- Finding previously unknown/unlabelled subgroups in the data
- Estimating an average or a proportion from a representative sample and using that estimate to generalize to the broader population
HarvardX: CS50's Introduction to Artificial Intelligence with Python - This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, machine learning, large language models, and other topics in artificial intelligence as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.
Coursera: AI For Everyone (DeepLearning.AI) - In this course, you will learn:
The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science
- What AI realistically can--and cannot--do
- How to spot opportunities to apply AI to problems in your own organization
- What it feels like to build machine learning and data science projects
- How to work with an AI team and build an AI strategy in your company
- How to navigate ethical and societal discussions surrounding AI
Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI.
Google Cloud Skills Boost: Introduction to Generative AI - This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps.
DeepLearning.AI: ChatGPT Prompt Engineering for Developers - In ChatGPT Prompt Engineering for Developers, you will learn how to use a large language model (LLM) to quickly build new and powerful applications. Using the OpenAI API, you’ll be able to quickly build capabilities that learn to innovate and create value in ways that were cost-prohibitive, highly technical, or simply impossible before now. This short course taught by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI) will describe how LLMs work, provide best practices for prompt engineering, and show how LLM APIs can be used in applications for a variety of tasks, including:
- Summarizing (e.g., summarizing user reviews for brevity)
- Inferring (e.g., sentiment classification, topic extraction)
- Transforming text (e.g., translation, spelling & grammar correction)
- Expanding (e.g., automatically writing emails)
In addition, you’ll learn two key principles for writing effective prompts, how to systematically engineer good prompts, and also learn to build a custom chatbot. All concepts are illustrated with numerous examples, which you can play with directly in our Jupyter notebook environment to get hands-on experience with prompt engineering.
Google AI - Study the latest AI concepts and ML solutions to build intuitive, user-centric apps.
Coursera: Deep Learning Specialization (DeepLearning.AI) - The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.
LinkedIn Learning: What Is Generative AI? - Whether you work in film, marketing, healthcare, automobile, or real-estate, generative AI is changing the way your job is executed, and those who adapt early will reap its benefits sooner. All professions will be affected by generative AI. Its invention can be compared to the invention of photography, a true creative revolution. If you want to be part of the leaders that are advancing this revolution, this course can get you started on your learning journey. In this course, generative AI expert Pinar Seyhan Demirdag covers the basics of generative AI, with topics including what it is, how it works, how to create your own content, different types of models, future predictions, and ethical implications.
Open Lecture Series
Harvard COMPSCI 229r: Algorithms for Big Data (25 Lectures)
Stanford CS221: Artificial Intelligence: Principles and Techniques (19 Lectures)
Stanford CS224N: Natural Language Processing with Deep Learning (22 Lectures)
Stanford CS229: Machine Learning (20 Lectures) - Coursera Course
Stanford Lecture Collection: Convolutional Neural Networks for Visual Recognition (16 Lectures)
MIT 6.0002: Introduction to Computational Thinking and Data Science (15 Lectures)
MIT 6.S191: Introduction to Deep Learning (43 Lectures)
Steve Brunton Intro to Data Science (Short Videos)
AMBOSS Medical Statistics (Short Videos)
Kaggle Open Courses on Data Science
Kaggle is an online community of data scientists and machine learning experts that allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
Its free and open courses on data science pare down complex topics to their key practical components for learners to gain usable skills.
The courses include but not limited to:
- Intro to Machine Learning
- Data Visualization
- Deep Learning
- AI Ethics
- Geospatial Analysis
Coding Resources for Beginners
Data Science for Healthcare: Python Fundamentals
UBC Library Research Data Management: Anonymize and De-Identify
Some kinds of data are sensitive, and cannot be shared for legal or ethical reasons. This can include:
- Personal identifiers
- Sensitive ecological data
- Sacred or protected cultural practices
De-identification means removing identifying data from a dataset. Once a dataset has been de-identified, the dataset can be shared without disclosing identifying information.
Removing identifiers is important to protect the confidentiality of research participants. But there is always a risk of re-identifying data, and changing technology introduces new ways to re-identify data. Managing that risk is an important part of sharing research data.
UBC Library introduces several ways of approaching de-identification, each with its own benefits and drawbacks.
Privacy Matters @ UBC Focus On: Workshop Series
The Privacy and Information Security Management team is proud to offer ongoing Workshops to the UBC community. Each session focuses on a specific privacy and/or security topic, and is presented by subject matter experts of the chosen item.
Topics are chosen based upon community feedback, as well as current news items as they relate to UBC and UBC staff, faculty and researchers.