PPT-Why knowledge and learning are essential to achieving SDG6

The Civil Society Water Sanitation and Hygiene Fund Amanda Morgan on behalf of Bronwyn Powell September 2017 Global sanitation and water crisis In 2015 884 million people lacked a basic water supply . 6 94 319 539 634 736 264 5038 Agriculture 07 25 62 264 486 661 339 3180 Arabic 183 302 498 648 761 880 120 6010 Art 04 10 104 493 814 956 44 5090 Biology 75 186 417 623 752 862 138 7115 Business Studies 22 80 206 393 551 705 295 5070 Chemistry 93 190 Learnings. or Essential Outcomes. September 14, 2010. The Four Key Questions.  of PLC’s. What . is it we expect our students to learn? . How will we know when they have learned it? . How will we respond when they don’t learn? . Network. of the. SMYTH COUNTY SCHOOLS. . by . Dan Mulligan. , flexiblecreativity.com. March 2014. LET’S DO THIS VIRGINIA!. Proactive Strategies to Raise Student Achievement. RIGOR . with. . NURTURING. Experiential Hives. Presented by. Philip . Marsh. Group. . Managing . Director. B.Sc. (. Eng. ); . Pr.Eng. ; . Dipl.Bus.Man. ; G.D.E; M.S.A.I.C.E; M.I.C.E; . M.I.Struct.E. ; C. . Eng. Phone: +27 (0)11 958 0619. By . Dr. Hilarie B. Davis, . hilarie@techforlearning.org. Dr. Bradford T. Davey, . brad@techforlearning.org. . Objectives for our time together. 1) Understand what makes a question “essential”. 2) Understand the role essential questions can play in Next Gen teaching and learning. July 23, 2013. What Does it Mean To Be Successful?. My First Painting. My First Fish. Who Am I?. Mr. Fernando Morales. Human Being. Quantum Mechanic. Abstract Mathematician. Yoga Meditator. Student of MMA. and Secondary Education. 2015 MO-CASE FALL CONFERENCE . Presented by: Bonnie Aaron & Tiffani Muessig. September 2014. 1. PURPOSE. 2. To provide a basic overview of the New MAP-A developed by Dynamic Learning Maps consortium. ICUC Jacksonville District Management AdvisorFECA FraudAchieving Excellence Through KnowledgeCourse ObjectivesUnderstand the difference between fraud and abuseIdentify indicators used to recognize pot August 2002 2002 The IDEA Center some attention is given to them an selected on the FIF WHAT IS MEANT BY EACH IDEA OBJECTIVE It would be easier to describe objectives if they were mutually exclusive b machine learning implies that a machine will learn how to do something new . but this is . not quite accurate – what is it that the machine is to learn?. is there a process in place and the machine needs to learn domain knowledge? . Questions. By: DonReita Nelson, M.Ed.. Goals . To understand what an essential question is. To be able to write effective essential questions. An Essential Question . is:. A question . that lies at the heart of a subject or a curriculum . Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition--as well as some we don\'t yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as Big Data has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications. Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today\'s machine learning boom in context. He describes the basics of machine learning and some applications the use of machine learning algorithms for pattern recognition artificial neural networks inspired by the human brain algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of data science, and discusses the ethical and legal implications for data privacy and security. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition--as well as some we don\'t yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as Big Data has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications. Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today\'s machine learning boom in context. He describes the basics of machine learning and some applications the use of machine learning algorithms for pattern recognition artificial neural networks inspired by the human brain algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of data science, and discusses the ethical and legal implications for data privacy and security. A framework for achieving market Innovations through sponsored R&D. Joseph P. Lane. Center on Knowledge Translation for Technology Transfer,. Center on Technology Transfer. School of Public Health & Health Professions.