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  • Presentation 
    Sampling Table Configurations for the Hierarchical Poisson-Dirichlet Process

    Sampling Table Configurations for the Hierarchical Poisson-Dirichlet Process

    This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Athens 2011. Hierarchical modeling and reasoning are fundamental in machine intelligence, and for this the two-parameter Poisson-Dirichlet Process (PDP) plays an important role. The most popular MCMC sampling algorithm for the hierarchical PDP and hierarchical Dirichlet Process is to conduct an incremental sampling based on the Chinese restaurant metaphor, which originates from the Chinese restaurant process (CRP). In this paper, with the same metaphor, we propose a new table representation for the hierarchical PDPs by introducing an auxiliary latent variable, called table indicator, to record which customer takes responsibility for starting a new table. In this way, the new representation allows full exchangeability that is an essential condition for a correct Gibbs sampling algorithm. Based on this representation, we develop a block Gibbs sampling algorithm, which can jointly sample the data item and its table contribution. We test this out on the hierarchical Dirichlet process variant of latent Dirichlet allocation (HDP-LDA) developed by Teh, Jordan, Beal and Blei. Experiment results show that the proposed algorithm outperforms their "posterior sampling by direct assignment" algorithm in both out-of-sample perplexity and convergence speed. The representation can be used with many other hierarchical PDP models.

    Subjects:
    Computing
    Keywords:
    Machine learning Artificial intelligence
    Resource Type:
    Presentation
     
    OER
  • Others 
    Papers With Code: The latest in machine learning

    Papers With Code: The latest in machine learning

    The mission of Papers With Code is to create a free and open resource with Machine Learning papers, code and evaluation tables.We believe this is best done together with the community and powered by automation.

    Course related:
    COMP5121 Data Mining and Data Warehousing Applications, COMP5212 Software Design and Architecture, COMP5123 Intelligent Information Systems, COMP5222 Software Testing and Quality Assurance, and COMP5131 Introduction to Information Systems
    Subjects:
    Computing
    Keywords:
    Machine learning Artificial intelligence
    Resource Type:
    Others
     
  • MOOC 
    Machine Learning Specialization

    Machine Learning Specialization

    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
     
  • MOOC 
    Machine Learning

    Machine Learning

    Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

    Course related:
    EIE6207 Theoretical Fundamental and Engineering Approaches for Intelligent Signal and. Information Processing and COMP4434 Big Data Analytics
    Subjects:
    Computing
    Keywords:
    Artificial intelligence Machine learning
    Resource Type:
    MOOC
     
  • Others 
    Kaggle

    Kaggle

    Kaggle offers a no-setup, customizable, Jupyter Notebooks environment. Access GPUs at no cost to you and a huge repository of community published data & code. Inside Kaggle you’ll find all the code & data you need to do your data science work. Use over 50,000 public datasets and 400,000 public notebooks to conquer any analysis in no time.

    Subjects:
    Computing
    Keywords:
    Machine learning Artificial intelligence Big data
    Resource Type:
    Others
     
  • Video 
    Introduction to PolyU GenAI Platform (Teacher Session)

    Introduction to PolyU GenAI Platform (Teacher Session)

    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)

    Subjects:
    Learning Analytics, Good Practices, and Assessment & Feedback
    Keywords:
    Artificial intelligence Artificial intelligence -- Moral ethical aspects Generative programming (Computer science) Machine learning Artificial intelligence -- Educational applications
    Resource Type:
    Video
     
  • Video 
    Introduction to PolyU GenAI Platform (Student Session)

    Introduction to PolyU GenAI Platform (Student Session)

    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)

    Subjects:
    Learning Analytics, Good Practices, and Assessment & Feedback
    Keywords:
    Artificial intelligence Artificial intelligence -- Moral ethical aspects Generative programming (Computer science) Machine learning Artificial intelligence -- Educational applications
    Resource Type:
    Video
     
  • Others 
    Incorporating and organizing OER for learning AI (OER4AI) practically with fun

    Incorporating and organizing OER for learning AI (OER4AI) practically with fun

    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
     
  • Video 
    Generative AI in Teaching, Learning and Assessment: Issues and Opportunities

    Generative AI in Teaching, Learning and Assessment: Issues and Opportunities

    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)

    Subjects:
    Learning Analytics, Good Practices, and Assessment & Feedback
    Keywords:
    Artificial intelligence Educational tests measurements Generative programming (Computer science) Machine learning Artificial intelligence -- Educational applications
    Resource Type:
    Video
     
  • Video 
    Examples of assessment design that expect student engagement with GenAI and deepen

    Examples of assessment design that expect student engagement with GenAI and deepen

    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)

    Subjects:
    Learning Analytics, Good Practices, and Assessment & Feedback
    Keywords:
    Artificial intelligence -- Educational applications Educational tests measurements Generative programming (Computer science) Machine learning Artificial intelligence
    Resource Type:
    Video