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OER4AI features a collection of public resources on AI, with categorization of different AI topics. With this OER portal, teachers can use its teaching materials, while students can access it and attempt its exercises. We aim at:
• Providing materials for students to gain hands-on experience;
• Collecting the public resources on AI;
• Giving teachers access to this website through PolyU OER Portal.
- Subjects:
- Computing
- Keywords:
- Machine learning Machine learning -- Study teaching Artificial intelligence Artificial intelligence -- Study teaching
- Resource Type:
- Others
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Video
Interested in harnessing the power of Generative AI (GenAI) for your studies? Join us in exploring the GenAI platform, its functionality and usage policies in our upcoming workshop. Learn about how GenAI can enhance your learning experience and how to employ it in your studies while maintaining data privacy and security. We'll introduce you to 'prompts engineering' and emphasise the importance of academic integrity in the context of AI technology usage. Come and join this workshop co-organised by EDC and ITS.
Event Date: 27/9/2023
Facilitator(s): Chan, Dick (EDC), Mark, Kai Pan (EDC), Tam, Barbara (EDC), Leung, Rian (ITS)
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Video
Curious about integrating Generative AI (GenAI) into your teaching methodologies? Embark on a journey with EDC and ITS in a comprehensive workshop introducing the innovative GenAI platform. This session will guide you through the platform's operations, explaining its usage policies. During the workshop, we'll briefly discuss the need for redesigning our assessment strategies in sync with this advanced tool to optimise learning outcomes effectively. Even more importantly, we will discuss data security and privacy concerns surrounding GenAI usage. This workshop offers an unrivalled opportunity to expand your understanding and proficiency in using AI in an educational context. If you're prepared to explore the cutting edge of education technology, then this is the ideal workshop for you.
Event Date: 20/9/2023
Facilitator(s): Chan, Dick (EDC), Mark, Kai Pan (EDC), Tam, Barbara (EDC), Leung, Rian (ITS)
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Video
Come hear three very different examples of assessment design that fully expect students to consult GenAI. They aim to deepen learning experiences by requiring students to produce multimodal submissions, revisit particular key points discussed in class, and demonstrate their understanding via hands-on quizzes and lab notebooks. When the assessment focus changes, the assessment criteria may change accordingly, and this will be included in the workshop.
Event Date: 30/8/2023
Facilitator: Chen, Julia (EDC)
Speaker(s): Chu, Rodney (APSS), Chan, Dick (EDC), Cheung, Gary (ABCT), Robbins, Jane (ELC)
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Video
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)
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Video
The rapid development and widening availability of generative AI tools to create and refine content presents a huge opportunity to re-assess some of the key foundational assumptions and practices behind the ways that our courses are designed and delivered.
In this seminar, Dr Bates will share his views on educators’ obligations to engage with these issues, educate students (and ourselves) on the affordances and limitations of new and emerging AI tools, iteratively experiment in a space that is rapidly changing, and share the successes (and failures) of UBC colleagues.
Dr Bates will also present some practical advice for different ways in which generative AI tools may be incorporated into teaching activities and assessments and outline ways in which UBC is gearing up to support instructors in these efforts.
Event Date: 9/8/2023
Presenter: Bates, Simon (Vice-Provost and Associate Vice-President, Teaching and Learning, Pro Tem, Professor of Teaching, Department of Physics and Astronomy, The University of British Columbia (UBC), Canada),
Facilitator(s): Lo, Dawn (EDC), Chon, Leo (EDC)
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Video
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
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Video
Psychology, Computer Science and Neuroscience have a history of shared questions and inter-related advances. Recently, new technology has enabled those fields to move from “toy” small-scale approaches to the study of language learning from raw sensory input and to do so at a large scale that constitutes daily life. The three primary goals of my research are 1) to quantify the statistical regularities in the real world, 2) to examine the underlying computational mechanisms operated on the statistical data, and 3) to apply the findings from basic science to real-world applications. In this talk, I will present several projects in my research lab to show that the advances in human learning and machine learning fields place us at the tipping point for powerful and consequential new insights into mechanisms of (and algorithms for) learning.
Event Date: 28/06/2023
Speaker: Prof. Chen YU (University of Texas at Austin)
Hosted by: Faculty of Humanities
- Subjects:
- Language and Languages
- Keywords:
- Machine learning Language acquisition Computational linguistics
- Resource Type:
- Video
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Video
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
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Video
EDC is organising a series of Sharing Sessions that present departmental project deliverables and innovations in Technology Enhanced Learning, promoting sustainable and impactful practices that resonate across PolyU and beyond, and funded by PolyU’s Quality Incentive Scheme on Online Teaching, Stage I.
This session proudly presents four departments:
EE: VR, AR & machine learning by Dr Fung Yu-fai
LIB: Using DataCamp to Support Online Learning and Teaching of Data Literacy by Mr Ernest Lam
LMS: Gamification and simulation-based teaching by Dr Anthony Pang
SLLO & COMP: Metaverse and virtual learning platforms by Dr Grace Ngai
Event Date: 15/2/2023
Presenter(s): Dr Yu-fai Fung (EE), Mr Ernest Lam (LIB), Dr Anthony Pang (LMS), Dr Grace Ngai (SLLO)
Facilitator(s): Mr Roy Kam (EDC)
- Subjects:
- Lesson Design and Good Practices
- Keywords:
- College teaching Web-based instruction Internet in education Lesson planning Educational technology
- Resource Type:
- Video
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Courseware
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
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Video
With calculus well behind us, it's time to enter the next major topic in any study of mathematics. Linear Algebra! The name doesn't sound very intimidating, but there are some pretty abstract concepts in this subject. Let's start nice and easy simply by learning about what this subject covers and some basic terminology.
- Course related:
- COMP4434 Big Data Analytics
- Subjects:
- Mathematics and Statistics
- Keywords:
- Algebras Linear
- 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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Others
This project was started in 2007 as a Google Summer of Code project by David Cournapeau. Later that year, Matthieu Brucher started work on this project as part of his thesis. In 2010 Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort and Vincent Michel of INRIA took leadership of the project and made the first public release, February the 1st 2010. Since then, several releases have appeared following a ~ 3-month cycle, and a thriving international community has been leading the development.
- Course related:
- EIE6207 Theoretical Fundamental and Engineering Approaches for Intelligent Signal and Information Processing
- Subjects:
- Computing
- Keywords:
- Machine learning Python (Computer program language)
- Resource Type:
- Others
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Others
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
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Video
This channel walks you through the entire process of learning to code in Python; all the way from basics to advanced machine learning and deep learning. The primary emphasis will be on image processing and other relevant functionality. Why did I create this channel? To help you (students and researchers) gain a new skill and succeed in your respective fields.
You may think coding is hard and that it's not your cup of tea, but Python made it easy to code even advanced algorithms. In addition, coding will make you self sufficient, it will teach you how to think, it improves your collaborative skills and it can take your career to new heights. Therefore, if you want to stay ahead of your peers and relevant in your field, overcome your fears and start coding!
- Subjects:
- Computing
- Keywords:
- Computer programming Computer science Python (Computer program language)
- Resource Type:
- Video
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Video
Machine learning can deliver unprecedented performance. Its application domain has expanded into safety-critical cyber-physical systems such as UAVs and self-driver cars. However, the safety assurance of vehicular control has two conditions: 1) an analytical model of system behaviors such as provable stability, and 2) the software safety certification process (e.g., DO 178C) requires that the software be simple enough so that software safety can be validated by a combination of model checking and near exhaustive testing. Although ML software, as is, does not meet these two safety requirements, the real-time physics model supervised ML architecture holds the promise to 1) meet the two safety requirements and 2) enable ML software to safely improve control performance and safely learn from its experience in real-time. This talk will review the structure of the proposed architecture and some methods to embed physics into ML-enabled CPS control.
Event Date: 12/05/2022
Speaker: Prof. Lui Sha (University of Illinois Urbana-Champaign)
Hosted by: Graduate School
- Subjects:
- Aeronautical and Aviation Engineering and Computing
- Keywords:
- Machine learning Vehicles Remotely piloted Computer software -- Reliability Drone aircraft
- Resource Type:
- Video
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Video
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
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Video
Tanmay Bakshi realized that in order to prevent suicide, we need a better way of detecting patterns. For the last 3 years, Tanmay and his team have been developing an app that can pick up on irregularities in a person's online behavior to build an early warning systems for at-risk teens. His hope is that this app will help get teens in distress the help they need, when they need it most.
- Subjects:
- Sociology and Social Work and Human Services
- Keywords:
- Suicidal behavior -- Risk factors Teenagers -- Suicidal behavior -- Prevention Internet teenagers
- Resource Type:
- Video
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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
-
Video
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
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Others
I am fortunate to be among the very first NTU EECS professors to offer two Mandarin-teaching MOOCs (massive open online courses) on NTU@Coursera. The two MOOCs are Machine Learning Foundations (Mathematical, Algorithmic) and Machine Learning Techniques and are based on the textbook Learning from Data: A Short Course that I co-authored. The book is consistently among the best sellers in Machine Learning on Amazon.
The slides of the MOOCs below are available as is with no explicit or implied warranties. The slides themselves are shared by CC-BY-NC 3.0, but the copyright of all materials (figures in particular) remain with the original copyright holder (in almost all cases the authors of the Learning from Data: A Short Course book).
- Course related:
- COMP4432 Machine Learning
- Subjects:
- Computing
- Keywords:
- Machine learning
- Resource Type:
- Others
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MOOC
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
-
Video
Welcome to Zero to Hero for Natural Language Processing using TensorFlow! If you’re not an expert on AI or ML, don’t worry -- we’re taking the concepts of NLP and teaching them from first principles with our host Laurence Moroney.
- Subjects:
- Computing
- Keywords:
- Machine learning Natural language processing (Computer science)
- Resource Type:
- Video
-
Others
Colaboratory, or "Colab" for short, allows you to write and execute Python in your browser, with
(1) Zero configuration required
(2) Free access to GPUs
(3) Easy sharing
Whether you're a student, a data scientist or an AI researcher, Colab can make your work easier. Watch Introduction to Colab to learn (https://www.youtube.com/watch?v=inN8seMm7UI) more, or just get started below!
- Course related:
- COMP3011 Machine Learning and Data Analytics
- Subjects:
- Computing
- Keywords:
- Computer programming Computer science Python (Computer program language)
- Resource Type:
- Others
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Video
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
-
Video
This self-learning video, How to operate a vinyl cutter, is a companion learning resource for the Vinyl cutter DIY manual. Using cutting paper stickers as an example, you will learn how to operate the vinyl cutter at the PolyU Library's i-Space.
- Keywords:
- Hbooks manuals Cutting machines
- Resource Type:
- Video
-
Video
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
-
MOOC
Learn the core ideas in machine learning, and build your first models.
- Course related:
- ENG2002 Computer Programming
- Subjects:
- Computing
- Keywords:
- Machine learning
- Resource Type:
- MOOC
-
Video
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
-
MOOC
In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini project. This course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not.
- Subjects:
- Computing
- Keywords:
- Artificial intelligence
- Resource Type:
- MOOC
-
MOOC
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
-
Video
Professor Strang describes independent vectors and the column space of a matrix as a good starting point for learning linear algebra. His outline develops the five shorthand descriptions of key chapters of linear algebra.
- Course related:
- COMP4432 Machine Learning
- Subjects:
- Mathematics and Statistics
- Keywords:
- Algebras Linear
- Resource Type:
- Video
-
Video
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
-
MOOC
Those who work in modern language service industry are required to be capable of using computers and Internet to aid their translation job by adapting a variety of efficient tools, rather than just using word processor tools and several basic computer-aided translation software. This course teaches the basic concepts of computer-aided translation technology, helps students learn to use a variety of computer-aided translation tools, enhances their ability to engage in various kinds of language service in such a technical environment, and helps them understand what the modern language service industry looks like. This course covers introduction to modern language services industry, basic principles and concepts of translation technology, information technology used in the process of language translation, how to use electronic dictionaries, Internet resources and corpus tools, practice of different computer-aided translation tools, translation quality assessment, basic concepts of machine translation, globalization, localization and so on. As a compulsory course for students majoring in Translation and Interpreting, this course is also suitable for students with or without language major background. By learning this course, students can better understand modern language service industry and their work efficiency will be improved for them to better deliver translation service. The course is one of the PKU-DeTao MOOCs, which is a joint effort by Peking University and DeTao Masters Academy.
- Subjects:
- Translating and Interpreting and Computing
- Keywords:
- Machine translating Translating interpreting
- Resource Type:
- MOOC
-
Video
Gradient Descent is the workhorse behind most of Machine Learning. When you fit a machine learning method to a training dataset, you're probably using Gradient Descent. It can optimize parameters in a wide variety of settings. Since it's so fundamental to Machine Learning, I decided to make a "step-by-step" video that shows you exactly how it works.
- Course related:
- COMP4434 Big Data Analytics
- Subjects:
- Computing
- Keywords:
- Machine learning Computer algorithms
- Resource Type:
- Video
-
Video
Explore statistical analysis with SPSS. Topics covered include how to create and analyze charts, build reports, import spreadsheets, create regression models, and export presentation graphics.
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Others
IEEE is the world’s largest technical professional organization dedicated to advancing technology for the benefit of humanity. IEEE and its members inspire a global community to innovate for a better tomorrow through its more than 396,000 members in over 160 countries, and its highly cited publications, conferences, technology standards, and professional and educational activities. IEEE is the trusted “voice” for engineering, computing, and technology information around the globe.
- Course related:
- ENG1003 Freshman Seminar for Engineering
- Subjects:
- Electronic and Information Engineering and Electrical Engineering
- Keywords:
- Electronic engineering Automatic control. Electronics
- Resource Type:
- Others
-
Video
An example of using least squares to do data fitting. In this example, we demonstrate how to 1) fit a straight line using ordinary least-squares method, and 2) estimate value of a new input based on the fitted line.
- Course related:
- LSGI3242A Digital Terrain Modelling
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Least squares Regression analysis
- Resource Type:
- Video
-
Video
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
-
Video
This video is take an adapted version of this course as part of the Stanford Artificial Intelligence Professional Program.
- Course related:
- AP619 Microfabrication Laboratory
- Subjects:
- Computing
- Keywords:
- Machine learning Computer algorithms
- Resource Type:
- Video
-
e-book
This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity.
- Subjects:
- Land Surveying and Geo-Informatics
- Keywords:
- Smart cities Cities towns -- Data processing Cities towns -- Remote sensing Big data
- Resource Type:
- e-book
-
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
-
Video
Statistics, Machine Learning and Data Science can sometimes seem like very scary topics, but since each technique is really just a combination of small and simple steps, they are actually quite simple. My goal with StatQuest is to break down the major methodologies into easy to understand pieces. That said, I don't dumb down the material. Instead, I build up your understanding so that you are smarter.
- Course related:
- HTI34016 Introduction to Clinical Research
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Statistics Mathematical analysis Data mining Machine learning
- Resource Type:
- Video
-
Courseware
Stanford Engineering Everywhere (SEE) expands the Stanford experience to students and educators online and at no charge. A computer and an Internet connection are all you need. The SEE course portfolio includes one of Stanford's most popular sequences: the three-course Introduction to Computer Science, taken by the majority of Stanford’s undergraduates, as well as more advanced courses in artificial intelligence and electrical engineering.
- Course related:
- EE1D01 Electrical Science for Everyone
- Subjects:
- Electronic and Information Engineering, Biomedical Engineering, Mechanical Engineering, and Computing
- Keywords:
- Engineering Computer science
- Resource Type:
- Courseware
-
Others
Scikit Learn provide simple and efficient tools for predictive data analysis. Assessible to everybody, and reusable in various contexts. It built on NumPy, SciPy, and matplotlib. It is open sources, commercially usable under the BSD License.
- Subjects:
- Computing
- Keywords:
- Python (Computer program language)
- Resource Type:
- Others
-
Video
This youtube playlist included the topic of deep learning for human language processing, linear algebra, deep reinforcement learning, generative adversarial network, deep learning theory, structured learning, and machine learning.
- Course related:
- LGT6801 Guided Study in Logistics I
- Subjects:
- Computing
- Keywords:
- Machine learning Natural language processing (Computer science)
- Resource Type:
- Video
-
Others
W3Schools is optimized for learning, testing, and training. Examples might be simplified to improve reading and basic understanding. Tutorials, references, and examples are constantly reviewed to avoid errors, but we cannot warrant full correctness of all content.
- Subjects:
- Computing
- Keywords:
- Web publishing Web site development Web sites -- Design
- Resource Type:
- Others
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MOOC
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
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MOOC
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