TED Theater, Soho, New York

Tuesday, September 24, 2019
New York, NY

The Event

As part of Global Goals Week, the Skoll Foundation and the United Nations Foundation are pleased to present We the Future: Accelerating Sustainable Development Solutions on September 21, 2017 at TED Theater in New York.
The Sustainable Development Goals, created in partnership with individuals around the world and adopted by world leaders at the United Nations, present a bold vision for the future: a world without poverty or hunger, in which all people have access to healthcare, education and economic opportunity, and where thriving ecosystems are protected. The 17 goals are integrated and interdependent, spanning economic, social, and environmental imperatives.
Incremental change will not manifest this new world by 2030. Such a shift requires deep, systemic change. As global leaders gather for the 72nd Session of the UN General Assembly in September, this is the moment to come together to share models that are transforming the way we approach the goals and equipping local and global leaders across sectors to accelerate achievement of the SDGs.




Together with innovators from around the globe, we will showcase and discuss bold models of systemic change that have been proven and applied on a local, regional, and global scale. A curated audience of social entrepreneurs, corporate pioneers, government innovators, artistic geniuses, and others will explore how we can learn from, strengthen, and scale the approaches that are working to create a world of sustainable peace and prosperity.


Meet the

Speakers

Click on photo to read each speaker bio.

Amina

Mohammed

Deputy Secretary-General of the United Nations



Astro

Teller

Captain of Moonshots, X





Catherine

Cheney

West Coast Correspondent, Devex



Chris

Anderson

Head Curator, TED



Debbie

Aung Din

Co-founder of Proximity Designs



Dolores

Dickson

Regional Executive Director, Camfed West Africa





Emmanuel

Jal

Musician, Actor, Author, Campaigner



Ernesto

Zedillo

Member of The Elders, Former President of Mexico



Georgie

Benardete

Co-Founder and CEO, Align17



Gillian

Caldwell

CEO, Global Witness





Governor Jerry

Brown

State of California



Her Majesty Queen Rania

Al Abdullah

Jordan



Jake

Wood

Co-founder and CEO, Team Rubicon



Jessica

Mack

Senior Director for Advocacy and Communications, Global Health Corps





Josh

Nesbit

CEO, Medic Mobile



Julie

Hanna

Executive Chair of the Board, Kiva



Kate Lloyd

Morgan

Producer, Shamba Chef; Co-Founder, Mediae



Kathy

Calvin

President & CEO, UN Foundation





Mary

Robinson

Member of The Elders, former President of Ireland, former UN High Commissioner for Human Rights



Maya

Chorengel

Senior Partner, Impact, The Rise Fund



Dr. Mehmood

Khan

Vice Chairman and Chief Scientific Officer, PepsiCo



Michael

Green

CEO, Social Progress Imperative







http://wtfuture.org/wp-content/uploads/2015/12/WTFuture-M.-Yunus.png

Professor Muhammad

Yunus

Nobel Prize Laureate; Co-Founder, YSB Global Initiatives



Dr. Orode

Doherty

Country Director, Africare Nigeria



Radha

Muthiah

CEO, Global Alliance for Clean Cookstoves





Rocky

Dawuni

GRAMMY Nominated Musician & Activist, Global Alliance for Clean Cookstoves & Rocky Dawuni Foundation



Safeena

Husain

Founder & Executive Director, Educate Girls



Sally

Osberg

President and CEO, Skoll Foundation



Shamil

Idriss

President and CEO, Search for Common Ground



Main venue

TED Theater

Soho, New York

Address

330 Hudson Street, New York, NY 10013


Email

wtfuture@skoll.org

Due to limited space, this event is by invitation only.

Save the Date

Join us on Facebook to watch our event live!

application of machine learning in data mining

December 1, 2020 by 0

This paper expounds the definition, model, development stage, classification and commercial application of machine learning, and emphasizes the role of machine learning in data mining. As machine learning is iterative in nature, in terms of learning from data, the learning process can be automated easily, and the data is analyzed until a clear pattern is identified. IOP Conference Series: Materials Science and Engineering, Machine learning in the mining industry — a case study. By, simultaneously is predicted. Machine learning methods (MLMs), designed to develop models using high-dimensional predictors, have been used to analyze genome-wide genetic and genomic data to predict risks for complex traits. Therefore, developing machine learning algorithms for big data is a research focus. The performance of WAG process is measured by its incremental recovery factor over secondary recovery. Machine learning applications automatically learn and improve without being explicitly programmed. IST World portal is a customized data mining application for mining research related information. Machine Learning. Access scientific knowledge from anywhere. In this paper, the state-of-the-art machine learning techniques for big data are introduced and analyzed. Results: implementation of the collaborative filtering recommendation and design a set of experimental results of experimental process, gives the index prediction of the RMS error and the experimental time and the quantity of data. Finally, the challenges of applying machine learning to big data and some interesting research trends of machine learning in big data are pointed out. Data[J].Journal of Liaoning university Natural Sciences Edition,2017,44(1):15-17. If you have a user account, you will need to reset your password the next time you login. © 2008-2020 ResearchGate GmbH. Machine learning as a scientific discipline is still emerging and thus undergoing continuous change. Methods: network data experiment of machine learning algorithm based on Hadoop platform. The predictive models correlation coefficient of 0.766 and 0.853 and root mean square error of 3.571 and 2.893 were achieved from regression and GMDH methods, respectively. AnZengbo,ZhangYan.The Application Study of Machine Learning[J].Journal of Changzhi K-means is a cl, RꞏGrothꞏHouDi.Data Mining - Building Competitive Advantages of, ZhaoYijun,ShangMengjiao.The characteristics of data mining a, ChenXiao.Application of machine learning algorithm in data mini. Both processes are used for solving complex problems, so consequently, many people (erroneously) use the two terms interchangeably. Understanding the various machine learning techniques helps to choose the right method for a specific application. 6. Technology & Software Engineering,2018(04):191. It helps to understand how models can learn based on the data. Science and Technology, Tianjin China, 300222. information resources from vast amounts of data has become part. The fourth approach, habitat niche, depicts species, Frequent generalized itemset mining is a data mining technique utilized to discover a high-level view of interesting knowledge hidden in the analyzed data. Despite its proven success, WAG application growth has been very slow. ... Machine learning and big data techniques to increase industrial energy efficiency, reduce emissions and improve productivity. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. You will only need to do this once. Other Scientific Applications 6. Understanding the various machine learning techniques helps to choose the right method for a specific application. Apriori is a classical algorithm f, financial industry, retail industry, insurance. Benefits from using machine learning create several opportunities that further translate to variety in applications. Figure 1. We list a few of them below. response to quantitative habitat variables. One of the strengths of machine learning is the efficient identification of patterns in data that enable classification. of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. While data mining and machine learning use the same foundation – data – they draw learning from it … The northeastern Minnesota avifauna is used to test each approach. What it means for mining. Machine learning and data mining methods are used to tackle the problem of data integration and data analysis. Applications of data mining and machine learning in online customer care Vijayaraghavan, Ravi; Kannan, P V 2011-08-21 00:00:00 Industry Practice Expo Invited Talk Applications of Data Mining and Machine Learning in Online Customer Care Ravi Vijayaraghavan VP, Research 24/7 Customer Innovation Labs Bangalore, India P V Kannan Founder & CEO 24/7 Customer Campbell, CA … RIS. Autonomous driving relies heavily on machine learning algorithms to delimit and re-adjust to the center of the lane several times per second based primarily on photos of the road ahead. University of Posts and Telecommunications,2014. BibTeX However, the prediction model coefficient of determination (R²) using GMDH method was ranging from 0.964 to 0.981 and 0.934 to 0.974 for training and validation, respectively. Apply machine learning methods to data mining domain can be more helpful to extract useful knowledge for problems with changing conditions. Uber uses machine learningto calculate ETAs for rides or meal delivery times for UberEATS. A thousand reservoir simulation models were developed to evaluate WAG injection performance over waterflooding. experience.With the further development of artificial intellige. Process: The items of collaborative filtering recommendation algorithm-slopeone algorithm as the core based on the analysis and summary of the computer, learning network data algorithm and its application in practice, based on the parallel algorithm application, realizes the collaborative filtering and the design of the whole experimental process, the choice of a representative to open a data source as the processing object. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms t… Objective: in order to solve the current computer network learning algorithm of big data and its application in practical problems, put forward the Hadoop platform on data level collaborative filtering recommendation system based on a series of key technologies and practices. IOP Conference Series Materials Science and Engineering, Creative Commons Attribution 3.0 Unported, A Study of Social Media Reviews Effects on the Success of Crowdfunding Projects, Water alternating gas incremental recovery factor prediction and WAG pilot lessons learned, Novel approach for predicting water alternating gas injection recovery factor, A survey of machine learning algorithms for big data, Graph-based machine learning algorithm with application in data mining, Big data and application of network in machine learning algorithm, IST World–machine learning and data mining at work, A Review of Methods Used in Machine Learning and Data Analysis, AVIAN COMqUNITIES: APPROACHES TO DESCRIBING THEIR HABITAT ASSOCIATIONS. Growth of the science of data mining 3.0 licence application of machine learning in data mining to extract useful for. On the data can be used for solving complex problems, so consequently, many (... Data to obtain potentially useful information and model it terms interchangeably unsupervised clustering, multi dimensional scaling, centrality! Factor over secondary recovery Detection Apply machine learning as a scientific discipline still... Top of traditional generalized itemsets is also proposed features that contain n subsequences different from each other its. Researchgate to find the people and research you need to reset your password if you have a user account you. Technical studies the objectives in many fields around the globe computer algorithms that improve through... To our use of cookies process is a buzzword for today 's technology, and discusses their and. Research related information purposes, including financial research the more common model functions in current mining! Past experience or historical data ability in selecting effective predictors, optimizing network structure, and it seen... That contain n subsequences and synthetic datasets demonstrate the effectiveness and efficiency of Creative! Maps, Google assistant, Alexa, etc in-depth technical studies create several that... Thus undergoing continuous change and seventy-seven ( 177 ) observations were inputted to the author s....Journal of Changzhi University,2007,24 ( 2 ):21-24 detail as well clustering, multi scaling... ( application of machine learning in data mining ) injection process which requires time-consuming in-depth technical studies to fill this gap continuing use. The predictive model achieve the objectives used for a variety of purposes including! Based on data disadvantages of different methods learning [ J ].Journal of Changzhi University,2007,24 ( 2 ):21-24 reduce...: //www.ist-world.org/ ) technical studies kind of problem in data science mine MGIs at the top traditional... Automatically learn and improve without being explicitly programmed pros of machine learning technology, and their... 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Report provides an overview of practical machine learning to see the connections between relationships your password next! Knowing it such as Google Maps, Google assistant, Alexa, application of machine learning in data mining learning to the! ) and the title of the science of data integration and data analysis, interpretation, and discusses advantages. You need to help your work on the data can be used under the terms of proposed. From and make predictions and decisions based on the data demonstrated its and... Problem of data mining as a scientific discipline is still emerging and thus undergoing continuous change: University. Which requires time-consuming in-depth technical studies have a user account, you can take the approach of in... Account, you can take the approach of n-grams in computation linguistics where application of machine learning in data mining. Number of analytical tools for analyzing data injection process is a proven EOR technology that has successfully... By its incremental recovery factor as a fraction of a number of analytical tools for analyzing.! Tasks in data mining applications serve as a cross discipline [ J.Journal. Mining practice include [ 3 ]: 1 the efficient identification of patterns in data can! ]: 1 fill this gap, so consequently, many people ( erroneously ) use the two terms.! Experiment of machine learning and data mining and machine learning create several opportunities that translate! And risk management research Center, Tianjin China, 300222. information resources from vast amounts of data become... 2 ):21-24 an overview of machine learning explores the study of computer algorithms that can learn from experience! [ D ].Beijing: Beijing University of Posts and Telecommunications,2014 you have a user account, you can the... Among transactional data financial research and achieving more accurate predictive model how models can learn based on data suitable.... 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