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Video
The PolyU Academy for Interdisciplinary Research (PAIR) of The Hong Kong Polytechnic University (PolyU) today hosted its inaugural Public Forum for Research and Innovation. Titled “DeepSeek and Beyond”, the keynote speech was delivered by Prof. YANG Hongxia, Associate Dean (Global Engagement) of the PolyU Faculty of Computer and Mathematical Sciences and Professor of the Department of Computing, who highlighted the latest developments in artificial intelligence (AI). The event attracted over a thousand participants, including faculty members, students, alumni, and leaders from the innovation and technology sector, as well as academics and the public. Additionally, over 390,000 viewers tuned in through the live streaming platforms.
The Forum began with a welcoming speech delivered by Prof. CHEN Qingyan, Director of PAIR and Chair Professor of Building Thermal Science of the PolyU Department of Building Environment and Energy Engineering. This was followed by Prof. ZHANG Chenqi, Chair Professor of Artificial Intelligence of the PolyU Department of Data Science and Artificial Intelligence, and Director of the PolyU Shenzhen Research Institute introducing the speaker.
Prof. Zhang said, “The development of large models is at the core of competition in the AI wave. DeepSeek has demonstrated that high-performance AI models can be achieved using fewer and less advanced graphics processing units (GPUs), demonstrating that cutting-edge AI technology can be realised through the optimisation of algorithms.”
The large AI model developed by the mainland Chinese startup DeepSeek has garnered wide acclaim around the world for its low-cost, high-performance, and open-source framework, disrupting the traditional “computing power-first” logic of AI model training. At the Forum, Prof. Yang highlighted the potential of generative AI (GenAI), adding that it presents abundant opportunities for various sectors, including healthcare, finance, manufacturing, retail, media and fashion, and for applications in medical imaging analysis, fraud detection, predictive maintenance, retail inventory management, content creation, and design and marketing.
Prof. Yang also recounted the evolution of AI and shared her professional milestones with the audience, notably the development of the M6 large model, which trained a 10-trillion-parameters model using just 512 GPUs. Prof. Yang further elaborated on how her GenAI project, Co-GenAI, improves the accessibility of AI technology while minimising dependence on large-scale centralised computing resources, thereby transforming the trajectory of AI progress. This ground-breaking effort has positioned Hong Kong and the Mainland at the forefront of global advancement in GenAI.
Moderated by Prof. Zhang Chenqi, a panel discussion was also held, featuring esteemed panellists Prof. Yang Hongxia and Prof. LI Qing, Head and Chair Professor of Data Science of the PolyU Department of Computing, and Co-Director of the Research Centre for Digital Transformation of Tourism. The scholars discussed the opportunities and challenges that advancements in AI present for higher education and research. They also engaged in fruitful discussion with participants during the question-and-answer session. The topics included the application of AI in industry, the regulation of information, its impact on the employment environment and economic development, and the integration of AI technologies.
PolyU is committed to advancing AI education and research. In January 2025, the University established the Faculty of Computer and Mathematical Sciences with a vision to lead global advancements in digital transformation and AI through distinguished education, research, and knowledge transfer.
Event date: 11/03/2025
Speaker: Prof. YANG Hongxia
Hosted by: PolyU Academy for Interdisciplinary Research
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Others
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.
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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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Others
This project was started in 2007 as a Google Summer of Code project by David Cournapeau. Later that year, Matthieu Brucher started work on this project as part of his thesis. In 2010 Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort and Vincent Michel of INRIA took leadership of the project and made the first public release, February the 1st 2010. Since then, several releases have appeared following a ~ 3-month cycle, and a thriving international community has been leading the development.
- Course related:
- EIE6207 Theoretical Fundamental and Engineering Approaches for Intelligent Signal and Information Processing
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Python (Computer program language) Machine learning
- Resource Type:
- Others
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Others
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, Data Science and Artificial Intelligence
- Keywords:
- Artificial intelligence Big data Machine learning
- Resource Type:
- Others
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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
Welcome to Zero to Hero for Natural Language Processing using TensorFlow! If you’re not an expert on AI or ML, don’t worry -- we’re taking the concepts of NLP and teaching them from first principles with our host Laurence Moroney.
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Natural language processing (Computer science) Machine learning
- Resource Type:
- Video
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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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MOOC
Learn the core ideas in machine learning, and build your first models.
- Course related:
- ENG2002 Computer Programming
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Machine learning
- Resource Type:
- MOOC
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