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Machine learning
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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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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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MOOC
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:
- COMP4434 Big Data Analytics and EIE6207 Theoretical Fundamental and Engineering Approaches for Intelligent Signal and. Information Processing
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Artificial intelligence Machine learning
- Resource Type:
- MOOC