Search Constraints

Number of results to display per page
Results for: Machine Learning Remove constraint Machine Learning

Search Results

  • 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
     
  • MOOC 
    AI For Everyone

    AI For Everyone

    AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take. In this course, you will learn: - The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science - What AI realistically can--and cannot--do - How to spot opportunities to apply AI to problems in your own organization - What it feels like to build machine learning and data science projects - How to work with an AI team and build an AI strategy in your company - How to navigate ethical and societal discussions surrounding AI

    Subjects:
    Computing
    Keywords:
    Artificial intelligence
    Resource Type:
    MOOC
     
  • 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
  • Video 
    Assessment re-designs for the generative AI era

    Assessment re-designs for the generative AI era

    Re-designing assessments within the context of generative AI is one of the most urgent challenges for universities. Might assessment re-design represent opportunities to build on key principles underpinning ‘good assessment’? Dependent on the disciplinary context, these might include iterative sequences of rich tasks; the development of student evaluative expertise; and linkages to real-world outcomes.
    Effective assessment sequences are sometimes time-consuming. By reducing assessment overload, we can create much-needed space for new possibilities: increased authentic assessment; assessments that involve critical engagement with generative AI outputs; an enhanced role for digital and interactive oral assessment; teacher and student co-learning in partnerships for assessment re-design; and assessing process as well as product. The thorny issues of academic integrity and ethical use of generative AI also merit attention but should not distract from a primary focus on the development of student learning.
    Generative AI raises exciting possibilities, yet there are few clear answers. In this workshop, complementary and alternative views, including those from different disciplinary perspectives will be welcomed.
    Event Date: 22/8/2023
    Speaker: Carless, David (Professor at the Faculty of Education, HKU)
    Facilitator(s): Chen, Julia (EDC), Chon, Leo (EDC)

    Subjects:
    Learning Analytics, Good Practices, and Assessment & Feedback
    Keywords:
    Artificial intelligence Educational tests measurements Generative programming (Computer science) Machine learning Artificial intelligence -- Educational applications
    Resource Type:
    Video
     
  • Video 
    But what is a Neural Network? | Deep learning, chapter 1

    But what is a Neural Network? | Deep learning, chapter 1

    This video introduces the basic concept and the overall working process of the ANN models, especially how the backpropogation works. How to establish a deep neuro network.

    Course related:
    LGT6801 Guided Study in Logistics I
    Subjects:
    Computing
    Keywords:
    Machine learning Neural networks (Computer science)
    Resource Type:
    Video
     
  • Courseware 
    CS229 - Machine Learning

    CS229 - Machine Learning

    This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

    Subjects:
    Computing
    Keywords:
    Pattern perception -- Statistical methods Machine learning
    Resource Type:
    Courseware
     
  • Courseware 
    CS50’s Introduction to Artificial Intelligence with Python

    CS50’s Introduction to Artificial Intelligence with Python

    This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.

    Course related:
    COMP3011 Design and Analysis of Algorithms, COMP1001 Problem Solving Methodology in Information Technology, COMP4434 Artificial Intelligence, and COMP2011 Data Structures
    Subjects:
    Human-Computer Interaction and Computing
    Keywords:
    Computer programming Computer science Python (Computer program language) Artificial intelligence
    Resource Type:
    Courseware
     
  • 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
     
  • Video 
    Can robots be creative? - Gil Weinberg

    Can robots be creative? - Gil Weinberg

    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.

    Keywords:
    Creative ability Artificial intelligence
    Resource Type:
    Video
     
  • Others 
    Codecademy: Learn to Code for Free

    Codecademy: Learn to Code for Free

    Learn to Code for Free. We're here to make coding more accessible, so everyone can learn the skills they need to upgrade their careers. For example, you can learn Python, HTML, CSS, and JavaScript.

    Subjects:
    Computing
    Keywords:
    Computer programming Programming languages (Electronic computers)
    Resource Type:
    Others
     
  • Video 
    Crash Course Artificial Intelligence

    Crash Course Artificial Intelligence

    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
    Keywords:
    Human-Computer Interaction Machine learning Artificial intelligence
    Resource Type:
    Video
     
  • Video 
    Crash Course Computer Science

    Crash Course Computer Science

    In 40 episodes, Carrie Anne Philbin teaches you computer science! This course is based on introductory college-level material as well as the AP Computer Science Principles guidelines. By the end of this course, you will be able to:

    *Outline the history of computers and the design decisions that gave us modern computers

    *Describe the basic elements of programming and software

    *Identify the basic components of computer hardware and what they do

    *Describe how computers are used and how that has evolved over time

    *Appreciate how far computers have come and how far they might take us

    Course related:
    AMA2222 Principles of Programming
    Subjects:
    Computing
    Keywords:
    Computer science
    Resource Type:
    Video
     
  • Video 
    Creating Full Colour Stickers with Vinyl Cutter

    Creating Full Colour Stickers with Vinyl Cutter

    This self-learning video, Creating Full Colour Stickers with Vinyl Cutter, is a beginner guide for students who want to make colour printed stickers with the Vinyl Cutter in Digital Makerspace. You will learn about:
    - How a vinyl cutter helps you to produce stickers
    - The steps to use Adobe Illustrator to create stickers from sample graphics and images provided
    - The Vinyl Cutting Service at the PolyU Library's i-Space

    Keywords:
    Stickers Adobe Illustrator (Computer file) Hbooks manuals Cutting machines
    Resource Type:
    Video
     
  • MOOC 
    Data Science

    Data Science

    Data science has critical applications across most industries, and is one of the most in-demand careers in computer science. Data scientists are the detectives of the big data era, responsible for unearthing valuable data insights through analysis of massive datasets. And just like a detective is responsible for finding clues, interpreting them, and ultimately arguing their case in court, the field of data science encompasses the entire data life cycle. That starts with capturing lots of raw data using data collection techniques, and then building and maintaining data pipelines and data warehouses that efficiently “clean” the data and make it accessible for analysis at scale. This data infrastructure allows data scientists to efficiently process datasets using data mining and data modeling skills, as well as analyze these outputs with sophisticated techniques like predictive analysis and qualitative analysis. Finally, these findings must be presented using data visualization and data reporting skills to help business decision makers. Depending on the size of the company, data scientists may be responsible for this entire data life cycle, or they might specialize in a particular portion of the life cycle as part of a larger data science team

    Subjects:
    Computing
    Keywords:
    Machine learning Data mining Big data
    Resource Type:
    MOOC
     
  • 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
     
  • MOOC 
    Data Science for Construction, Architecture and Engineering

    Data Science for Construction, Architecture and Engineering

    The building industry is exploding with data sources that impact the energy performance of the built environment and health and well-being of occupants. Spreadsheets just don’t cut it anymore as the sole analytics tool for professionals in this field. Participating in mainstream data science courses might provide skills such as programming and statistics, however the applied context to buildings is missing, which is the most important part for beginners. This course focuses on the development of data science skills for professionals specifically in the built environment sector. It targets architects, engineers, construction and facilities managers with little or no previous programming experience. An introduction to data science skills is given in the context of the building life cycle phases. Participants will use large, open data sets from the design, construction, and operations of buildings to learn and practice data science techniques. Essentially this course is designed to add new tools and skills to supplement spreadsheets. Major technical topics include data loading, processing, visualization, and basic machine learning using the Python programming language, the Pandas data analytics and sci-kit learn machine learning libraries, and the web-based Colaboratory environment. In addition, the course will provide numerous learning paths for various built environment-related tasks to facilitate further growth.

    Keywords:
    City planning -- Statistical methods Python (Computer program language) Information visualization
    Resource Type:
    MOOC
     
  • Others 
    Deep Learning

    Deep Learning

    Use TensorFlow to take machine learning to the next level. Your new skills will amaze you.

    Subjects:
    Computing
    Keywords:
    Python (Computer program language) Machine learning
    Resource Type:
    Others
     
  • 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
     
  • Video 
    Design High Speed Motor PMSM with ANSYS Electronic v18.2( part3)

    Design High Speed Motor PMSM with ANSYS Electronic v18.2( part3)

    How to Design High Speed Motor 100kw PMSM with ANSYS Electronic v18.2 ( ansys Maxwell 3D )

    Subjects:
    Electronic and Information Engineering and Electrical Engineering
    Keywords:
    ANSYS (Computer system) Computer-aided engineering
    Resource Type:
    Video
     
  • Video 
    Dialog with a World Class AI expert: Know HOW to get the MOST out of generative

    Dialog with a World Class AI expert: Know HOW to get the MOST out of generative

    A world class AI expert will explain to us in layman terms the power and limitations of generative AI tools, key factors that affect their performances and what users should know before deciding to use these tools and when reviewing the responses from these tools. Be inspired to discover more applications of these tools!
    Event Date: 13/6/2023
    Presenter: Prof. Usama Fayyad, Executive Director of the Institute for Experiential AI, Khoury College of Computer Sciences, Northeastern University, USA
    Facilitator(s): Eric Tsui (EDC), Ioanna Pavlidou (ITS)

    Keywords:
    Machine learning Artificial intelligence Education -- Effect of technological innovations on
    Resource Type:
    Video