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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
  • e-book 
    Artificial Intelligence

    Artificial Intelligence

    Artificial intelligence (AI) is a potent buzzword and happening technology which has greatly impacted the lifestyle of every human being either directly or indirectly and is shaping the future of tomorrow. In fact, AI is fast becoming an intrinsic part of our daily life and is not confined to university research labs, even if remarkable progress has been made in this domain. The benefit of this phenomenon is widely recognized in diversified areas, ranging from medicine to security to consumer applications and business, and resulting in improvements in the quality of life of humankind. Every new disruptive technology has its own pros and cons and AI is no exception to this rule. Privacy, data protection, and the rights of individuals pose social and ethical challenges.

    Subjects:
    Computing
    Keywords:
    Artificial intelligence
    Resource Type:
    e-book
     
  • Courseware 
    Artificial Intelligence

    Artificial Intelligence

    This course includes interactive demonstrations which are intended to stimulate interest and to help students gain intuition about how artificial intelligence methods work under a variety of circumstances.

    Subjects:
    Computing
    Keywords:
    Artificial intelligence
    Resource Type:
    Courseware
     
  • Others 
    OER

    Deep Learning in GeoSpatial Artificial Intelligence

    Geospatial artificial intelligence sometimes referred to as geoAI is recently receiving so much attention. From large-scale projects to smaller projects. GeoAI can be referred to as using artificial intelligence with Geographical information system to analyse and produce solution-based predictions.

    Subjects:
    Computing and Land Surveying and Geo-Informatics
    Keywords:
    Geospatial data Geographic information systems Artificial intelligence
    Resource Type:
    Others
     
  • Video 
    Can robots be creative?

    Can robots be creative?

    People have been grappling with the question of artificial creativity -- alongside the question of artificial intelligence -- for over 170 years. For instance, could we program machines to create high quality original music? And if we do, is it the machine or the programmer that exhibits creativity? Gil Weinberg investigates this creative conundrum.

    Subjects:
    Electronic and Information Engineering
    Keywords:
    Robotics Artificial intelligence
    Resource Type:
    Video
     
  • 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

    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
     
  • MOOC 
    Deep Learning Specialization. Master Deep Learning, and Break into AI

    Deep Learning Specialization. Master Deep Learning, and Break into AI

    If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning. In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach. You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice. AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work. We will help you master Deep Learning, understand how to apply it, and build a career in AI.

    Course related:
    AMA564 Deep Learning
    Subjects:
    Computing
    Keywords:
    Machine learning Neural networks (Computer science) Artificial intelligence
    Resource Type:
    MOOC
     
  • MOOC 
    Elements of AI

    Elements of AI

    The Elements of AI is a series of free online courses created by Reaktor and the University of Helsinki. We want to encourage as broad a group of people as possible to learn what AI is, what can (and can’t) be done with AI, and how to start creating AI methods. The courses combine theory with practical exercises and can be completed at your own pace.

    Course related:
    COMP4431 Artificial Intelligence
    Subjects:
    Computing
    Keywords:
    Artificial intelligence Machine learning
    Resource Type:
    MOOC
     
  • e-book 
    The Crystal Ball Instruction Manual Volume One: Introduction to Data Science

    The Crystal Ball Instruction Manual Volume One: Introduction to Data Science

    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