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e-book
A perfect introduction to the exploding field of Data Science for the curious, first-time student. The author brings his trademark conversational tone to the important pillars of the discipline: exploratory data analysis, choices for structuring data, causality, machine learning principles, and introductory Python programming using open-source Jupyter Notebooks. This engaging read will allow any dedicated learner to build the skills necessary to contribute to the Data Science revolution, regardless of background.
- Subjects:
- Computing
- Keywords:
- Data mining Computer science Artificial intelligence Textbooks
- Resource Type:
- e-book
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Video
On the TED@BCG stage, AI pathfinder Philipp Gerbert dispels the myth of AI as a complex and mysterious tool for business. In reality, he says, even those of us outside Silicon Valley can have an intimate understanding of AI and put it to work today. Gerbert walks us through the ABC's of AI and what it can mean for your organization.
- Keywords:
- Business enterprises -- Technological innovations Artificial intelligence
- Resource Type:
- Video
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Video
What happens when technology knows more about us than we do? Poppy Crum studies how we express emotions -- and she suggests the end of the poker face is near, as new tech makes it easy to see the signals that give away how we're feeling. In a talk and demo, she shows how "empathetic technology" can read physical signals like body temperature and the chemical composition of our breath to inform on our emotional state. For better or for worse. "If we recognize the power of becoming technological empaths, we get this opportunity where technology can help us bridge the emotional and cognitive divide," Crum says.
- Subjects:
- Technology, Electronic and Information Engineering, and Mechnical Engineering
- Keywords:
- Emotion recognition Artificial intelligence
- Resource Type:
- Video
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Video
In this ‘Student Voices’ session, students share their views on, and experiences with, emerging Generative AI tools, including ChatGPT. The session will provide a conversation opportunity between teachers and students in this rapidly changing area, and a valuable chance to hear the learner's perspective.
Event Date: 23/3/2023
Facilitator(s): Dr Julia Chen (EDC), Mr Anthony Ho (EDC)
Honourable moderator for Q&A session: Prof David Shum, Dean of FHSS
EDC Coordinator: Darren Harbutt
- Subjects:
- Lesson Design
- Keywords:
- Artificial intelligence College teaching Artificial intelligence -- Educational applications
- Resource Type:
- Video
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Video
In this lecture, we consider strategies for adversarial games such as chess. We discuss the minimax algorithm, and how alpha-beta pruning improves its efficiency. We then examine progressive deepening, which ensures that some answer is always available.
- Course related:
- COMP4431 Artificial Intelligence
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Artificial intelligence
- Resource Type:
- Video
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Presentation
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
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Video
Join colleagues from the Department of Computing (COMP) and the English Language Centre (ELC) as they share their insights, experiences, challenges and plans on redesigning assessments in response to the emergence of generative AI. In this webinar, participants will learn how PolyU staff are adapting their assessment strategies to incorporate AI-generated content, while still maintaining academic integrity and ensuring student learning outcomes are met. This session will provide valuable perspectives for educators who are interested in leveraging AI in their own teaching and assessment practices.
Event Date: 30/5/2023
Facilitator(s): Richard Lui (COMP), Adam Forrester (ELC), Mitesh Patel (EDC)
- Subjects:
- Student Engagement, Assessment & Feedback, Lesson Design, and Good Practices
- Keywords:
- Artificial intelligence Computer-assisted instruction Education -- Effect of technological innovations on Educational technology
- Resource Type:
- Video
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Others
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
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Video
In this lecture, Prof. Sifakis will discuss the relevance of existing criteria for comparing human and machine intelligence and show some notable analogies and differences between scientific knowledge and that produced by neural networks. Emphasising that autonomy is an important step towards Artificial General Intelligence (AGI), he will present a characterisation of autonomous systems, and showing key differences with mental systems equipped with common sense knowledge and reasoning, and advocate challenging work directions, including the development of a new foundation for systems engineering and scientific knowledge, and the joint exploration of physical and mental phenomena that embody human intelligence.
Even date: 3/3/2023
Speaker: Prof. Joseph Sifakis
Hosted by: PolyU Academy for Interdisciplinary Research
- Subjects:
- Human-Computer Interaction
- Keywords:
- Artificial intelligence
- 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
- Keywords:
- Machine learning Artificial intelligence
- Resource Type:
- MOOC
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