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OER4AI features a collection of public resources on AI, with categorization of different AI topics. With this OER portal, teachers can use its teaching materials, while students can access it and attempt its exercises. We aim at:
• Providing materials for students to gain hands-on experience;
• Collecting the public resources on AI;
• Giving teachers access to this website through PolyU OER Portal.
- Subjects:
- Computing
- Keywords:
- Machine learning Machine learning -- Study teaching Artificial intelligence Artificial intelligence -- Study teaching
- Resource Type:
- Others
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Video
Interested in harnessing the power of Generative AI (GenAI) for your studies? Join us in exploring the GenAI platform, its functionality and usage policies in our upcoming workshop. Learn about how GenAI can enhance your learning experience and how to employ it in your studies while maintaining data privacy and security. We'll introduce you to 'prompts engineering' and emphasise the importance of academic integrity in the context of AI technology usage. Come and join this workshop co-organised by EDC and ITS.
Event Date: 27/9/2023
Facilitator(s): Chan, Dick (EDC), Mark, Kai Pan (EDC), Tam, Barbara (EDC), Leung, Rian (ITS)
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Video
Curious about integrating Generative AI (GenAI) into your teaching methodologies? Embark on a journey with EDC and ITS in a comprehensive workshop introducing the innovative GenAI platform. This session will guide you through the platform's operations, explaining its usage policies. During the workshop, we'll briefly discuss the need for redesigning our assessment strategies in sync with this advanced tool to optimise learning outcomes effectively. Even more importantly, we will discuss data security and privacy concerns surrounding GenAI usage. This workshop offers an unrivalled opportunity to expand your understanding and proficiency in using AI in an educational context. If you're prepared to explore the cutting edge of education technology, then this is the ideal workshop for you.
Event Date: 20/9/2023
Facilitator(s): Chan, Dick (EDC), Mark, Kai Pan (EDC), Tam, Barbara (EDC), Leung, Rian (ITS)
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Video
Come hear three very different examples of assessment design that fully expect students to consult GenAI. They aim to deepen learning experiences by requiring students to produce multimodal submissions, revisit particular key points discussed in class, and demonstrate their understanding via hands-on quizzes and lab notebooks. When the assessment focus changes, the assessment criteria may change accordingly, and this will be included in the workshop.
Event Date: 30/8/2023
Facilitator: Chen, Julia (EDC)
Speaker(s): Chu, Rodney (APSS), Chan, Dick (EDC), Cheung, Gary (ABCT), Robbins, Jane (ELC)
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Video
Re-designing assessments within the context of generative AI is one of the most urgent challenges for universities. Might assessment re-design represent opportunities to build on key principles underpinning ‘good assessment’? Dependent on the disciplinary context, these might include iterative sequences of rich tasks; the development of student evaluative expertise; and linkages to real-world outcomes.
Effective assessment sequences are sometimes time-consuming. By reducing assessment overload, we can create much-needed space for new possibilities: increased authentic assessment; assessments that involve critical engagement with generative AI outputs; an enhanced role for digital and interactive oral assessment; teacher and student co-learning in partnerships for assessment re-design; and assessing process as well as product. The thorny issues of academic integrity and ethical use of generative AI also merit attention but should not distract from a primary focus on the development of student learning.
Generative AI raises exciting possibilities, yet there are few clear answers. In this workshop, complementary and alternative views, including those from different disciplinary perspectives will be welcomed.
Event Date: 22/8/2023
Speaker: Carless, David (Professor at the Faculty of Education, HKU)
Facilitator(s): Chen, Julia (EDC), Chon, Leo (EDC)
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Video
The rapid development and widening availability of generative AI tools to create and refine content presents a huge opportunity to re-assess some of the key foundational assumptions and practices behind the ways that our courses are designed and delivered.
In this seminar, Dr Bates will share his views on educators’ obligations to engage with these issues, educate students (and ourselves) on the affordances and limitations of new and emerging AI tools, iteratively experiment in a space that is rapidly changing, and share the successes (and failures) of UBC colleagues.
Dr Bates will also present some practical advice for different ways in which generative AI tools may be incorporated into teaching activities and assessments and outline ways in which UBC is gearing up to support instructors in these efforts.
Event Date: 9/8/2023
Presenter: Bates, Simon (Vice-Provost and Associate Vice-President, Teaching and Learning, Pro Tem, Professor of Teaching, Department of Physics and Astronomy, The University of British Columbia (UBC), Canada),
Facilitator(s): Lo, Dawn (EDC), Chon, Leo (EDC)
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Video
People have been grappling with the question of artificial creativity -- alongside the question of artificial intelligence -- for over 170 years. For instance, could we program machines to create high quality original music? And if we do, is it the machine or the programmer that exhibits creativity? Gil Weinberg investigates this creative conundrum.
- Keywords:
- Creative ability Artificial intelligence
- Resource Type:
- Video
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Video
A world class AI expert will explain to us in layman terms the power and limitations of generative AI tools, key factors that affect their performances and what users should know before deciding to use these tools and when reviewing the responses from these tools. Be inspired to discover more applications of these tools!
Event Date: 13/6/2023
Presenter: Prof. Usama Fayyad, Executive Director of the Institute for Experiential AI, Khoury College of Computer Sciences, Northeastern University, USA
Facilitator(s): Eric Tsui (EDC), Ioanna Pavlidou (ITS)
- Keywords:
- Machine learning Artificial intelligence Education -- Effect of technological innovations on
- Resource Type:
- Video
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Courseware
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, reinforcement learning, and other topics in artificial intelligence and machine learning 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.
- Course related:
- COMP3011 Design and Analysis of Algorithms, COMP1001 Problem Solving Methodology in Information Technology, COMP4434 Artificial Intelligence, and COMP2011 Data Structures
- Subjects:
- Human-Computer Interaction and Computing
- Keywords:
- Computer programming Computer science Python (Computer program language) Artificial intelligence
- Resource Type:
- Courseware
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MOOC
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.
This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.
It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
- Course related:
- AAE5103 Artificial Intelligence in Aviation Industry
- Subjects:
- Computing
- Keywords:
- Machine learning Artificial intelligence
- Resource Type:
- MOOC
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