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  • 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
     
  • 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 
    A First Look into AI+ Investment

    A First Look into AI+ Investment

    An online lecture on the topic of "A First Look into AI+ Investment".This lecture is suitable for secondary school and university students as well as the general public.

    Subjects:
    Finance
    Keywords:
    Artificial intelligence -- Forecasting Investments
    Resource Type:
    Video
     
  • Video 
    The Basics of AI for Business

    The Basics of AI for Business

    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
     
  • Video 
    How Humans And AI Can Work Together to Create Better Businesses

    How Humans And AI Can Work Together to Create Better Businesses

    Here's a paradox: as companies try to streamline their businesses by using artificial intelligence to make critical decisions, they may inadvertently make themselves less efficient. Business technologist Sylvain Duranton advocates for a "Human plus AI" approach -- using AI systems alongside humans, not instead of them -- and shares the specific formula companies can adopt to successfully employ AI while keeping humans in the loop.

    Keywords:
    Business enterprises -- Technological innovations Artificial intelligence
    Resource Type:
    Video
     
  • MOOC 
    Data Science Methodology

    Data Science Methodology

    Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand.
    This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand.
    Accordingly, in this course, you will learn:
    - The major steps involved in tackling a data science problem.
    - The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment.
    - How data scientists think!

    Course related:
    LGT6801 Guided Study in Logistics I, LGT6202: Stochastic Models and Decision under Uncertainty, LGT6802 Guided Study in Logistics II, and LGT6803: Guided Study in Logistics III
    Subjects:
    Business Information Technology and Computing
    Keywords:
    Electronic data processing Data mining Problem solving
    Resource Type:
    MOOC
     
  • Courseware 
    Fintech Online Short Course

    Fintech Online Short Course

    In this course, you will walk away with an up-to-date examination of the maturing FinTech industry and an understanding of the technologies set to shape the future of finance. Insight into who is currently adopting and driving financial technology innovation and the potential for partnerships between incumbents, start-ups and investors. The ability to critically assess the future of the financial services industry, through exploring complex real-world problems and how FinTech can be used to find solutions.A strategic framework to apply within your own role, and the opportunity to share this with like-minded professionals at an additional conference week.

    Course related:
    COMP4142 E-Payment and Cryptocurrency and COMP5521 Distributed Ledger Technology
    Subjects:
    Finance and Computing
    Keywords:
    Financial services industry -- Technological innovations Finance -- Technological innovations
    Resource Type:
    Courseware
     
  • Video 
    SAS: How to Tutorials - Advanced Analytics

    SAS: How to Tutorials - Advanced Analytics

    The free resources help you learn SAS quickly without a significant financial or time commitment. In Advanced Analytics module, you will explore the use of SAS in basic statistics, econometrics, forecasting, machine learning, optimization, and more.

    Subjects:
    Statistics and Research Methods
    Keywords:
    Statistics -- Data processing SAS (Computer file)
    Resource Type:
    Video
     
  • Presentation 
    Analyzing Word Frequencies in Large Text Corpora using Inter-arrival Times and Bootstrapping

    Analyzing Word Frequencies in Large Text Corpora using Inter-arrival Times and Bootstrapping

    This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Athens 2011. Comparing frequency counts over texts or corpora is an important task in many applications and scientific disciplines. Given a text corpus, we want to test a hypothesis, such as "word X is frequent", "word X has become more frequent over time", or "word X is more frequent in male than in female speech". For this purpose we need a null model of word frequencies. The commonly used bag-of-words model, which corresponds to a Bernoulli process with fixed parameter, does not account for any structure present in natural languages. Using this model for word frequencies results in large numbers of words being reported as unexpectedly frequent. We address how to take into account the inherent occurrence patterns of words in significance testing of word frequencies. Based on studies of words in two large corpora, we propose two methods for modeling word frequencies that both take into account the occurrence patterns of words and go beyond the bag-of-words assumption. The first method models word frequencies based on the spatial distribution of individual words in the language. The second method is based on bootstrapping and takes into account only word frequency at the text level. The proposed methods are compared to the current gold standard in a series of experiments on both corpora. We find that words obey different spatial patterns in the language, ranging from bursty to non-bursty/uniform, independent of their frequency, showing that the traditional approach leads to many false positives.

    Subjects:
    Management and Computing
    Keywords:
    Computational linguistics Text processing (Computer science) Discourse analysis -- Data processing
    Resource Type:
    Presentation
     
    OER