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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
Psychology, Computer Science and Neuroscience have a history of shared questions and inter-related advances. Recently, new technology has enabled those fields to move from “toy” small-scale approaches to the study of language learning from raw sensory input and to do so at a large scale that constitutes daily life. The three primary goals of my research are 1) to quantify the statistical regularities in the real world, 2) to examine the underlying computational mechanisms operated on the statistical data, and 3) to apply the findings from basic science to real-world applications. In this talk, I will present several projects in my research lab to show that the advances in human learning and machine learning fields place us at the tipping point for powerful and consequential new insights into mechanisms of (and algorithms for) learning.
Event Date: 28/06/2023
Speaker: Prof. Chen YU (University of Texas at Austin)
Hosted by: Faculty of Humanities
- Subjects:
- Language and Languages
- Keywords:
- Computational linguistics Language acquisition Machine learning
- Resource Type:
- Video
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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, Data Science and Artificial Intelligence
- Keywords:
- Artificial intelligence Machine learning
- Resource Type:
- MOOC
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Video
Machine learning can deliver unprecedented performance. Its application domain has expanded into safety-critical cyber-physical systems such as UAVs and self-driver cars. However, the safety assurance of vehicular control has two conditions: 1) an analytical model of system behaviors such as provable stability, and 2) the software safety certification process (e.g., DO 178C) requires that the software be simple enough so that software safety can be validated by a combination of model checking and near exhaustive testing. Although ML software, as is, does not meet these two safety requirements, the real-time physics model supervised ML architecture holds the promise to 1) meet the two safety requirements and 2) enable ML software to safely improve control performance and safely learn from its experience in real-time. This talk will review the structure of the proposed architecture and some methods to embed physics into ML-enabled CPS control.
Event Date: 12/05/2022
Speaker: Prof. Lui Sha (University of Illinois Urbana-Champaign)
Hosted by: Graduate School
- Subjects:
- Computing, Data Science and Artificial Intelligence and Aeronautical and Aviation Engineering
- Keywords:
- Machine learning Computer software -- Reliability Drone aircraft Vehicles Remotely piloted
- Resource Type:
- Video
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Others
I am fortunate to be among the very first NTU EECS professors to offer two Mandarin-teaching MOOCs (massive open online courses) on NTU@Coursera. The two MOOCs are Machine Learning Foundations (Mathematical, Algorithmic) and Machine Learning Techniques and are based on the textbook Learning from Data: A Short Course that I co-authored. The book is consistently among the best sellers in Machine Learning on Amazon.
The slides of the MOOCs below are available as is with no explicit or implied warranties. The slides themselves are shared by CC-BY-NC 3.0, but the copyright of all materials (figures in particular) remain with the original copyright holder (in almost all cases the authors of the Learning from Data: A Short Course book).
- Course related:
- COMP4432 Machine Learning
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Machine learning
- Resource Type:
- Others
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Video
In 20 episodes, Jabril will teach you about Artificial Intelligence and Machine Learning! This course is based on a university-level curriculum. By the end of the course, you will be able to: * Define, differentiate, and provide examples of Artificial Intelligence and three types of Machine Learning: supervised, unsupervised, and reinforcement * Understand how different AI and ML approaches can be combined to create compelling applications such as natural language processing, robotics, recommender systems, and web search * Implement several types of AI to classify images, generate text from examples, play video games, and recommend content based on past preferences * Understand the causes of algorithmic bias and audit datasets for several of these causes
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Artificial intelligence Human-Computer Interaction Machine learning
- Resource Type:
- Video
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