Support Vector Machines!
|
|
- Rosamond Woods
- 6 years ago
- Views:
Transcription
1 Support Vector Machines!
2 The Sorting Hat is sick today We need to help it sort the students!
3 But all we know is: Intelligence Bravery House (Gryffindor or Ravenclaw) ? 8 16? ? 4 12? ? 15 7? ? 10 3?
4 Let s look at what the Sorting Hat did before...
5 Instructions for how to plot data You have a list of people, each with an intelligence and bravery value, and house. For each person, write their intelligence and bravery value on a sticky note (use a pink sticky if they re from Gryffindor and a blue sticky if they re from Ravenclaw) Plot each sticky note on your paper
6 Previous students
7 Add new students to your plot (yellow stickies) Intelligence Bravery House (Gryffindor or Ravenclaw) ? 8 16? ? 4 12? ? 15 7? ? 10 3?
8 New students this year
9 Here are their houses Replace the yellow stickies with the correct color sticky. Now, use a yardstick to autosort new students! Students on one side will be sorted into Gryffindor. Students on the other side will be sorted into Ravenclaw.
10 Don t move your yardstick!
11 What does your yardstick tell you about these students? Intelligence: 16 Bravery: 15 Intelligence: 1 Bravery: 5.5
12 What does your yardstick tell you about these students?
13 What does your yardstick tell you about these students?
14 What does your yardstick tell you about these students?
15 This year s new students! Harry Potter Parvati Patil Ron Weasley Hermione Granger Dean Thomas Neville Longbottom Luna Lovegood Padma Patil Terry Boot Michael Corner
16 So what makes a good line?
17 So what makes a good line? Bad!
18 So what makes a good line? Bad!
19 So what makes a good line? Good? Why?
20 Maximum-margins!
21 Next year!
22 Welcome to another year at Hogwarts! After being sick last year, the sorting hat is starting to think that it might be time to retire... We are the new Sorting Hat in-training! We choose the house, then the Sorting hat will tell us if we got it right
23 Set up your yardstick, then don t move it...
24 Intelligence Bravery House (Gryffindor or Ravenclaw) Time to sort some students! (yellow stickies) 6 8? 8 4? 3 11? 12 14? 7 4? 6 2? ? 11 9? 9 1? 12 1?
25 Which houses do you think these students should be sorted into?
26 Here are the answers. How did you do?
27 So what makes a good line? (What should we do for next year?)
28 Last year of Sorting-Hat-apprenticeship
29 Set up your yardstick, then don t move it...
30 Intelligence Bravery House (Gryffindor or Ravenclaw) Time to sort some students! (yellow stickies) 6 5? 4 2.5? 10 10? 15 12? 14 9? 16 3? 14 12? 2 9? 5 1? 5 13?
31 Which houses do you think these students should be sorted into?
32 Here are the answers! How did you do?
33 So what makes a good line? (Which line was better?)
34 What if we had one more attribute (feature) of people that we were measuring? (How would we incorporate this data into our auto-sorter?) Intelligence Bravery Quidditch ability House Gryffindor (Ron Weasley) Gryffindor (Hermione Granger) Gryffindor (Harry Potter)
35 What if we had two more attributes (features) of people that we were measuring? (How would we incorporate this data into our auto-sorter?) Intelligence Bravery Quidditch ability Kindness House Gryffindor (Ron Weasley) Gryffindor (Hermione Granger) Gryffindor (Harry Potter)
36 Now that we have the power to learn patterns from data and apply that knowledge...
37 Let s sort beings into human versus non-human! You have a list of beings, their height, and whether they are human or nonhuman Put a piece of masking tape on your string for each being, and mark whether it is human or non-human Can you get 100% classification accuracy using your yardstick?
38 We can do this from 2D to 3D too! (...or 3D to 4D!...or 4D to 9D! )
39 Machine learning! Model patterns in data (using math!) Use models to infer information from new data
40 Support Vector Machines! (The math!) Support Vector Machine BIG = + margin Small errors = Balance between reducing errors versus making a bigger margin
41 Support Vector Machines! (The math!) Big C: Errors are REALLY BAD! Small C: Errors are ok, as long as we make the margin BIG
42 Support Vector Machines are powerful! Handwriting recognition! (Automated mail sorting) Image classification! (Cat or not?) Text document classification! (Automated spam filtering)
43 Machine learning is powerful!
44 Thanks for helping with sorting!
45 The sorting hat - Welcome to Hogwarts! - Every year the sorting hat sorts students into houses (e.g. Gryffindor and Ravenclaw). - But the sorting hat is sick today :(. - Your job is to help the sorting hat sort students. - How can we do this? - Lets look at how previous students were sorted.
46 How can we sort new students? - How can we use the data we plotted to sort this year s students? - We will now try and sort a few students!
47 How can we sort new students? Intelligence Bravery House XX XX?
48 How can we sort new students? Sorting-hat solution: Intelligence Bravery House XX XX Gryffindor (Harry Potter)
49 How can we sort new students? Sorting-hat solutions: Intelligence Bravery House XX XX Gryffindor (Ron Weasley) XX XX Ravenclaw (Luna Lovegood) XX XX Ravenclaw (Terry Boot) XX XX Ravenclaw (Padma Patil) XX XX Gryffindor (Parvati Patil) XX XX Gryffindor (Dean Thomas) XX XX Ravenclaw (Michael Corner)
50 Maximum margins! Here are two more students: Intelligence Bravery House XX XX? XX XX? - Does your classifier correctly sort these students? - How can we describe a best line using only the original students?
51 Maximum margins! Sorting-hat solutions: Intelligence Bravery House XX XX Gryffindor (Hermione Granger) XX XX Gryffindor (Neville Longbottom)
52 Sorting hat answers show some outliers (but still barely linearly separable) (i.e. Hermione and Neville) Make a new line We can choose to either try really hard to do what the sorting hat, or do a better job of following the sorting hat s overall pattern
53 Last round of 2-D classification
54 Humans versus non-humans
55 Some description of how non-linear SVM works?
56 How can we sort new students? Intelligence Bravery House XX XX? XX XX? XX XX? XX XX? XX XX? XX XX? XX XX?
J j W w. Write. Name. Max Takes the Train. Handwriting Letters Jj, Ww: Words with j, w 321
Write J j W w Jen Will Directions Have children write a row of each letter and then write the words. Home Activity Ask your child to write each letter and tell you how to make the letter. Handwriting Letters
More informationModule 12. Machine Learning. Version 2 CSE IIT, Kharagpur
Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should
More informationDetecting Wikipedia Vandalism using Machine Learning Notebook for PAN at CLEF 2011
Detecting Wikipedia Vandalism using Machine Learning Notebook for PAN at CLEF 2011 Cristian-Alexandru Drăgușanu, Marina Cufliuc, Adrian Iftene UAIC: Faculty of Computer Science, Alexandru Ioan Cuza University,
More informationStacks Teacher notes. Activity description. Suitability. Time. AMP resources. Equipment. Key mathematical language. Key processes
Stacks Teacher notes Activity description (Interactive not shown on this sheet.) Pupils start by exploring the patterns generated by moving counters between two stacks according to a fixed rule, doubling
More informationMulti-Lingual Text Leveling
Multi-Lingual Text Leveling Salim Roukos, Jerome Quin, and Todd Ward IBM T. J. Watson Research Center, Yorktown Heights, NY 10598 {roukos,jlquinn,tward}@us.ibm.com Abstract. Determining the language proficiency
More informationLecture 1: Basic Concepts of Machine Learning
Lecture 1: Basic Concepts of Machine Learning Cognitive Systems - Machine Learning Ute Schmid (lecture) Johannes Rabold (practice) Based on slides prepared March 2005 by Maximilian Röglinger, updated 2010
More informationIntroduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition
Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition Todd Holloway Two Lecture Series for B551 November 20 & 27, 2007 Indiana University Outline Introduction Bias and
More informationPython Machine Learning
Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled
More informationGenevieve L. Hartman, Ph.D.
Curriculum Development and the Teaching-Learning Process: The Development of Mathematical Thinking for all children Genevieve L. Hartman, Ph.D. Topics for today Part 1: Background and rationale Current
More informationHuman Emotion Recognition From Speech
RESEARCH ARTICLE OPEN ACCESS Human Emotion Recognition From Speech Miss. Aparna P. Wanare*, Prof. Shankar N. Dandare *(Department of Electronics & Telecommunication Engineering, Sant Gadge Baba Amravati
More informationLinking Task: Identifying authors and book titles in verbose queries
Linking Task: Identifying authors and book titles in verbose queries Anaïs Ollagnier, Sébastien Fournier, and Patrice Bellot Aix-Marseille University, CNRS, ENSAM, University of Toulon, LSIS UMR 7296,
More informationExperience College- and Career-Ready Assessment User Guide
Experience College- and Career-Ready Assessment User Guide 2014-2015 Introduction Welcome to Experience College- and Career-Ready Assessment, or Experience CCRA. Experience CCRA is a series of practice
More informationLesson 12. Lesson 12. Suggested Lesson Structure. Round to Different Place Values (6 minutes) Fluency Practice (12 minutes)
Objective: Solve multi-step word problems using the standard addition reasonableness of answers using rounding. Suggested Lesson Structure Fluency Practice Application Problems Concept Development Student
More informationEdexcel GCSE. Statistics 1389 Paper 1H. June Mark Scheme. Statistics Edexcel GCSE
Edexcel GCSE Statistics 1389 Paper 1H June 2007 Mark Scheme Edexcel GCSE Statistics 1389 NOTES ON MARKING PRINCIPLES 1 Types of mark M marks: method marks A marks: accuracy marks B marks: unconditional
More informationIntroduction to the Practice of Statistics
Chapter 1: Looking at Data Distributions Introduction to the Practice of Statistics Sixth Edition David S. Moore George P. McCabe Bruce A. Craig Statistics is the science of collecting, organizing and
More informationHardhatting in a Geo-World
Hardhatting in a Geo-World TM Developed and Published by AIMS Education Foundation This book contains materials developed by the AIMS Education Foundation. AIMS (Activities Integrating Mathematics and
More informationAlgebra 2- Semester 2 Review
Name Block Date Algebra 2- Semester 2 Review Non-Calculator 5.4 1. Consider the function f x 1 x 2. a) Describe the transformation of the graph of y 1 x. b) Identify the asymptotes. c) What is the domain
More informationEnd-of-Module Assessment Task K 2
Student Name Topic A: Two-Dimensional Flat Shapes Date 1 Date 2 Date 3 Rubric Score: Time Elapsed: Topic A Topic B Materials: (S) Paper cutouts of typical triangles, squares, Topic C rectangles, hexagons,
More informationWord Segmentation of Off-line Handwritten Documents
Word Segmentation of Off-line Handwritten Documents Chen Huang and Sargur N. Srihari {chuang5, srihari}@cedar.buffalo.edu Center of Excellence for Document Analysis and Recognition (CEDAR), Department
More informationGrade 2: Using a Number Line to Order and Compare Numbers Place Value Horizontal Content Strand
Grade 2: Using a Number Line to Order and Compare Numbers Place Value Horizontal Content Strand Texas Essential Knowledge and Skills (TEKS): (2.1) Number, operation, and quantitative reasoning. The student
More informationSwitchboard Language Model Improvement with Conversational Data from Gigaword
Katholieke Universiteit Leuven Faculty of Engineering Master in Artificial Intelligence (MAI) Speech and Language Technology (SLT) Switchboard Language Model Improvement with Conversational Data from Gigaword
More informationA Case Study: News Classification Based on Term Frequency
A Case Study: News Classification Based on Term Frequency Petr Kroha Faculty of Computer Science University of Technology 09107 Chemnitz Germany kroha@informatik.tu-chemnitz.de Ricardo Baeza-Yates Center
More informationRule Learning With Negation: Issues Regarding Effectiveness
Rule Learning With Negation: Issues Regarding Effectiveness S. Chua, F. Coenen, G. Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX Liverpool, United
More informationCHAPTER I INTRODUCTION. have no limitation at all in making communication with each other. Despite the
CHAPTER I INTRODUCTION A. Background of Research In recent times, different countries with different languages and cultures have no limitation at all in making communication with each other. Despite the
More informationPOWERTEACHER GRADEBOOK
POWERTEACHER GRADEBOOK FOR THE SECONDARY CLASSROOM TEACHER In Prince William County Public Schools (PWCS), student information is stored electronically in the PowerSchool SMS program. Enrolling students
More informationLecture 1: Machine Learning Basics
1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3
More informationFull text of O L O W Science As Inquiry conference. Science as Inquiry
Page 1 of 5 Full text of O L O W Science As Inquiry conference Reception Meeting Room Resources Oceanside Unifying Concepts and Processes Science As Inquiry Physical Science Life Science Earth & Space
More informationTwitter Sentiment Classification on Sanders Data using Hybrid Approach
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 4, Ver. I (July Aug. 2015), PP 118-123 www.iosrjournals.org Twitter Sentiment Classification on Sanders
More informationMike Cohn - background
Agile Estimating and Planning Mike Cohn August 5, 2008 1 Mike Cohn - background 2 Scrum 24 hours Sprint goal Return Return Cancel Gift Coupons wrap Gift Cancel wrap Product backlog Sprint backlog Coupons
More informationCS 446: Machine Learning
CS 446: Machine Learning Introduction to LBJava: a Learning Based Programming Language Writing classifiers Christos Christodoulopoulos Parisa Kordjamshidi Motivation 2 Motivation You still have not learnt
More informationSTT 231 Test 1. Fill in the Letter of Your Choice to Each Question in the Scantron. Each question is worth 2 point.
STT 231 Test 1 Fill in the Letter of Your Choice to Each Question in the Scantron. Each question is worth 2 point. 1. A professor has kept records on grades that students have earned in his class. If he
More informationAUTOMATED FABRIC DEFECT INSPECTION: A SURVEY OF CLASSIFIERS
AUTOMATED FABRIC DEFECT INSPECTION: A SURVEY OF CLASSIFIERS Md. Tarek Habib 1, Rahat Hossain Faisal 2, M. Rokonuzzaman 3, Farruk Ahmed 4 1 Department of Computer Science and Engineering, Prime University,
More informationOCR for Arabic using SIFT Descriptors With Online Failure Prediction
OCR for Arabic using SIFT Descriptors With Online Failure Prediction Andrey Stolyarenko, Nachum Dershowitz The Blavatnik School of Computer Science Tel Aviv University Tel Aviv, Israel Email: stloyare@tau.ac.il,
More informationPaper Reference. Edexcel GCSE Mathematics (Linear) 1380 Paper 1 (Non-Calculator) Foundation Tier. Monday 6 June 2011 Afternoon Time: 1 hour 30 minutes
Centre No. Candidate No. Paper Reference 1 3 8 0 1 F Paper Reference(s) 1380/1F Edexcel GCSE Mathematics (Linear) 1380 Paper 1 (Non-Calculator) Foundation Tier Monday 6 June 2011 Afternoon Time: 1 hour
More informationPowerTeacher Gradebook User Guide PowerSchool Student Information System
PowerSchool Student Information System Document Properties Copyright Owner Copyright 2007 Pearson Education, Inc. or its affiliates. All rights reserved. This document is the property of Pearson Education,
More informationWHEN THERE IS A mismatch between the acoustic
808 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 14, NO. 3, MAY 2006 Optimization of Temporal Filters for Constructing Robust Features in Speech Recognition Jeih-Weih Hung, Member,
More informationSight Word Assessment
Make, Take & Teach Sight Word Assessment Assessment and Progress Monitoring for the Dolch 220 Sight Words What are sight words? Sight words are words that are used frequently in reading and writing. Because
More informationLongest Common Subsequence: A Method for Automatic Evaluation of Handwritten Essays
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 6, Ver. IV (Nov Dec. 2015), PP 01-07 www.iosrjournals.org Longest Common Subsequence: A Method for
More informationHistorical maintenance relevant information roadmap for a self-learning maintenance prediction procedural approach
IOP Conference Series: Materials Science and Engineering PAPER OPEN ACCESS Historical maintenance relevant information roadmap for a self-learning maintenance prediction procedural approach To cite this
More informationMultivariate k-nearest Neighbor Regression for Time Series data -
Multivariate k-nearest Neighbor Regression for Time Series data - a novel Algorithm for Forecasting UK Electricity Demand ISF 2013, Seoul, Korea Fahad H. Al-Qahtani Dr. Sven F. Crone Management Science,
More informationReducing Features to Improve Bug Prediction
Reducing Features to Improve Bug Prediction Shivkumar Shivaji, E. James Whitehead, Jr., Ram Akella University of California Santa Cruz {shiv,ejw,ram}@soe.ucsc.edu Sunghun Kim Hong Kong University of Science
More informationAnalysis of Emotion Recognition System through Speech Signal Using KNN & GMM Classifier
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-issn: 2278-2834,p- ISSN: 2278-8735.Volume 10, Issue 2, Ver.1 (Mar - Apr.2015), PP 55-61 www.iosrjournals.org Analysis of Emotion
More informationRule Learning with Negation: Issues Regarding Effectiveness
Rule Learning with Negation: Issues Regarding Effectiveness Stephanie Chua, Frans Coenen, and Grant Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX
More informationThe University of Amsterdam s Concept Detection System at ImageCLEF 2011
The University of Amsterdam s Concept Detection System at ImageCLEF 2011 Koen E. A. van de Sande and Cees G. M. Snoek Intelligent Systems Lab Amsterdam, University of Amsterdam Software available from:
More informationAP Statistics Summer Assignment 17-18
AP Statistics Summer Assignment 17-18 Welcome to AP Statistics. This course will be unlike any other math class you have ever taken before! Before taking this course you will need to be competent in basic
More informationNotetaking Directions
Porter Notetaking Directions 1 Notetaking Directions Simplified Cornell-Bullet System Research indicates that hand writing notes is more beneficial to students learning than typing notes, unless there
More informationTEACHING Simple Tools Set II
TEACHING GUIDE TEACHING Simple Tools Set II Kindergarten Reading Level ISBN-10: 0-8225-6880-2 Green ISBN-13: 978-0-8225-6880-3 2 TEACHING SIMPLE TOOLS SET II Standards Science Mathematics Language Arts
More informationSchool Year 2017/18. DDS MySped Application SPECIAL EDUCATION. Training Guide
SPECIAL EDUCATION School Year 2017/18 DDS MySped Application SPECIAL EDUCATION Training Guide Revision: July, 2017 Table of Contents DDS Student Application Key Concepts and Understanding... 3 Access to
More informationRicopili: Postimputation Module. WCPG Education Day Stephan Ripke / Raymond Walters Toronto, October 2015
Ricopili: Postimputation Module WCPG Education Day Stephan Ripke / Raymond Walters Toronto, October 2015 Ricopili Overview Ricopili Overview postimputation, 12 steps 1) Association analysis 2) Meta analysis
More informationFrom understanding perspectives to informing public policy the potential and challenges for Q findings to inform survey design
Rachel Baker From understanding perspectives to informing public policy the potential and challenges for Q findings to inform survey design Organised session: Neil McHugh, Job van Exel Session outline
More informationA Comparison of Two Text Representations for Sentiment Analysis
010 International Conference on Computer Application and System Modeling (ICCASM 010) A Comparison of Two Text Representations for Sentiment Analysis Jianxiong Wang School of Computer Science & Educational
More informationProbability and Statistics Curriculum Pacing Guide
Unit 1 Terms PS.SPMJ.3 PS.SPMJ.5 Plan and conduct a survey to answer a statistical question. Recognize how the plan addresses sampling technique, randomization, measurement of experimental error and methods
More informationLip reading: Japanese vowel recognition by tracking temporal changes of lip shape
Lip reading: Japanese vowel recognition by tracking temporal changes of lip shape Koshi Odagiri 1, and Yoichi Muraoka 1 1 Graduate School of Fundamental/Computer Science and Engineering, Waseda University,
More informationProbabilistic Latent Semantic Analysis
Probabilistic Latent Semantic Analysis Thomas Hofmann Presentation by Ioannis Pavlopoulos & Andreas Damianou for the course of Data Mining & Exploration 1 Outline Latent Semantic Analysis o Need o Overview
More informationCase study Norway case 1
Case study Norway case 1 School : B (primary school) Theme: Science microorganisms Dates of lessons: March 26-27 th 2015 Age of students: 10-11 (grade 5) Data sources: Pre- and post-interview with 1 teacher
More informationInterpreting ACER Test Results
Interpreting ACER Test Results This document briefly explains the different reports provided by the online ACER Progressive Achievement Tests (PAT). More detailed information can be found in the relevant
More informationAnswer Key For The California Mathematics Standards Grade 1
Introduction: Summary of Goals GRADE ONE By the end of grade one, students learn to understand and use the concept of ones and tens in the place value number system. Students add and subtract small numbers
More informationSpeech Emotion Recognition Using Support Vector Machine
Speech Emotion Recognition Using Support Vector Machine Yixiong Pan, Peipei Shen and Liping Shen Department of Computer Technology Shanghai JiaoTong University, Shanghai, China panyixiong@sjtu.edu.cn,
More informationWE GAVE A LAWYER BASIC MATH SKILLS, AND YOU WON T BELIEVE WHAT HAPPENED NEXT
WE GAVE A LAWYER BASIC MATH SKILLS, AND YOU WON T BELIEVE WHAT HAPPENED NEXT PRACTICAL APPLICATIONS OF RANDOM SAMPLING IN ediscovery By Matthew Verga, J.D. INTRODUCTION Anyone who spends ample time working
More informationReinForest: Multi-Domain Dialogue Management Using Hierarchical Policies and Knowledge Ontology
ReinForest: Multi-Domain Dialogue Management Using Hierarchical Policies and Knowledge Ontology Tiancheng Zhao CMU-LTI-16-006 Language Technologies Institute School of Computer Science Carnegie Mellon
More informationIterative Cross-Training: An Algorithm for Learning from Unlabeled Web Pages
Iterative Cross-Training: An Algorithm for Learning from Unlabeled Web Pages Nuanwan Soonthornphisaj 1 and Boonserm Kijsirikul 2 Machine Intelligence and Knowledge Discovery Laboratory Department of Computer
More informationLEGO MINDSTORMS Education EV3 Coding Activities
LEGO MINDSTORMS Education EV3 Coding Activities s t e e h s k r o W t n e d Stu LEGOeducation.com/MINDSTORMS Contents ACTIVITY 1 Performing a Three Point Turn 3-6 ACTIVITY 2 Written Instructions for a
More information16.1 Lesson: Putting it into practice - isikhnas
BAB 16 Module: Using QGIS in animal health The purpose of this module is to show how QGIS can be used to assist in animal health scenarios. In order to do this, you will have needed to study, and be familiar
More informationQuickStroke: An Incremental On-line Chinese Handwriting Recognition System
QuickStroke: An Incremental On-line Chinese Handwriting Recognition System Nada P. Matić John C. Platt Λ Tony Wang y Synaptics, Inc. 2381 Bering Drive San Jose, CA 95131, USA Abstract This paper presents
More informationIncreasing Student Engagement
Increasing Student Engagement Description of Student Engagement Student engagement is the continuous involvement of students in the learning. It is a cyclical process, planned and facilitated by the teacher,
More informationTotalLMS. Getting Started with SumTotal: Learner Mode
TotalLMS Getting Started with SumTotal: Learner Mode Contents Learner Mode... 1 TotalLMS... 1 Introduction... 3 Objectives of this Guide... 3 TotalLMS Overview... 3 Logging on to SumTotal... 3 Exploring
More informationMission Statement Workshop 2010
Mission Statement Workshop 2010 Goals: 1. Create a group mission statement to guide the work and allocations of the Teen Foundation for the year. 2. Explore funding topics and areas of interest through
More informationShockwheat. Statistics 1, Activity 1
Statistics 1, Activity 1 Shockwheat Students require real experiences with situations involving data and with situations involving chance. They will best learn about these concepts on an intuitive or informal
More informationInformal Comparative Inference: What is it? Hand Dominance and Throwing Accuracy
Informal Comparative Inference: What is it? Hand Dominance and Throwing Accuracy Logistics: This activity addresses mathematics content standards for seventh-grade, but can be adapted for use in sixth-grade
More informationAQUA: An Ontology-Driven Question Answering System
AQUA: An Ontology-Driven Question Answering System Maria Vargas-Vera, Enrico Motta and John Domingue Knowledge Media Institute (KMI) The Open University, Walton Hall, Milton Keynes, MK7 6AA, United Kingdom.
More informationDublin City Schools Mathematics Graded Course of Study GRADE 4
I. Content Standard: Number, Number Sense and Operations Standard Students demonstrate number sense, including an understanding of number systems and reasonable estimates using paper and pencil, technology-supported
More informationCAN PICTORIAL REPRESENTATIONS SUPPORT PROPORTIONAL REASONING? THE CASE OF A MIXING PAINT PROBLEM
CAN PICTORIAL REPRESENTATIONS SUPPORT PROPORTIONAL REASONING? THE CASE OF A MIXING PAINT PROBLEM Christina Misailidou and Julian Williams University of Manchester Abstract In this paper we report on the
More informationEmporia State University Degree Works Training User Guide Advisor
Emporia State University Degree Works Training User Guide Advisor For use beginning with Catalog Year 2014. Not applicable for students with a Catalog Year prior. Table of Contents Table of Contents Introduction...
More informationELA/ELD Standards Correlation Matrix for ELD Materials Grade 1 Reading
ELA/ELD Correlation Matrix for ELD Materials Grade 1 Reading The English Language Arts (ELA) required for the one hour of English-Language Development (ELD) Materials are listed in Appendix 9-A, Matrix
More informationKindergarten Lessons for Unit 7: On The Move Me on the Map By Joan Sweeney
Kindergarten Lessons for Unit 7: On The Move Me on the Map By Joan Sweeney Aligned with the Common Core State Standards in Reading, Speaking & Listening, and Language Written & Prepared for: Baltimore
More informationHelping at Home ~ Supporting your child s learning!
Helping at Home ~ Supporting your child s learning! Halcombe School 2014 HELPING AT HOME At Halcombe School, we think teaching your child at school is like coaching your child in a sports team. When your
More informationActivities for School
Activities for School Label the School Label the school in the target language and then do a hide-n-seek activity using the directions in the target language. Label the Classroom I label my room (these
More informationSystem Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks
System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks 1 Tzu-Hsuan Yang, 2 Tzu-Hsuan Tseng, and 3 Chia-Ping Chen Department of Computer Science and Engineering
More informationAutomatic Speaker Recognition: Modelling, Feature Extraction and Effects of Clinical Environment
Automatic Speaker Recognition: Modelling, Feature Extraction and Effects of Clinical Environment A thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy Sheeraz Memon
More informationBackground Information. Instructions. Problem Statement. HOMEWORK INSTRUCTIONS Homework #3 Higher Education Salary Problem
Background Information Within higher education, faculty salaries have become a contentious issue as tuition rates increase and state aid shrinks. Competitive salaries are important for recruiting top quality
More informationLaboratorio di Intelligenza Artificiale e Robotica
Laboratorio di Intelligenza Artificiale e Robotica A.A. 2008-2009 Outline 2 Machine Learning Unsupervised Learning Supervised Learning Reinforcement Learning Genetic Algorithms Genetics-Based Machine Learning
More informationNovember 2012 MUET (800)
November 2012 MUET (800) OVERALL PERFORMANCE A total of 75 589 candidates took the November 2012 MUET. The performance of candidates for each paper, 800/1 Listening, 800/2 Speaking, 800/3 Reading and 800/4
More informationREAD 180 Next Generation Software Manual
READ 180 Next Generation Software Manual including ereads For use with READ 180 Next Generation version 2.3 and Scholastic Achievement Manager version 2.3 or higher Copyright 2014 by Scholastic Inc. All
More informationUniversity of Groningen. Systemen, planning, netwerken Bosman, Aart
University of Groningen Systemen, planning, netwerken Bosman, Aart IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document
More informationEnsemble Technique Utilization for Indonesian Dependency Parser
Ensemble Technique Utilization for Indonesian Dependency Parser Arief Rahman Institut Teknologi Bandung Indonesia 23516008@std.stei.itb.ac.id Ayu Purwarianti Institut Teknologi Bandung Indonesia ayu@stei.itb.ac.id
More informationExposé for a Master s Thesis
Exposé for a Master s Thesis Stefan Selent January 21, 2017 Working Title: TF Relation Mining: An Active Learning Approach Introduction The amount of scientific literature is ever increasing. Especially
More informationAlgebra 1, Quarter 3, Unit 3.1. Line of Best Fit. Overview
Algebra 1, Quarter 3, Unit 3.1 Line of Best Fit Overview Number of instructional days 6 (1 day assessment) (1 day = 45 minutes) Content to be learned Analyze scatter plots and construct the line of best
More informationUsing GIFT to Support an Empirical Study on the Impact of the Self-Reference Effect on Learning
80 Using GIFT to Support an Empirical Study on the Impact of the Self-Reference Effect on Learning Anne M. Sinatra, Ph.D. Army Research Laboratory/Oak Ridge Associated Universities anne.m.sinatra.ctr@us.army.mil
More informationBootstrapping Personal Gesture Shortcuts with the Wisdom of the Crowd and Handwriting Recognition
Bootstrapping Personal Gesture Shortcuts with the Wisdom of the Crowd and Handwriting Recognition Tom Y. Ouyang * MIT CSAIL ouyang@csail.mit.edu Yang Li Google Research yangli@acm.org ABSTRACT Personal
More informationAppendix L: Online Testing Highlights and Script
Online Testing Highlights and Script for Fall 2017 Ohio s State Tests Administrations Test administrators must use this document when administering Ohio s State Tests online. It includes step-by-step directions,
More informationUnit 1: Scientific Investigation-Asking Questions
Unit 1: Scientific Investigation-Asking Questions Standards: OKC 3 Process Standard 3: Experimental design - Understanding experimental designs requires that students recognize the components of a valid
More informationWest s Paralegal Today The Legal Team at Work Third Edition
Study Guide to accompany West s Paralegal Today The Legal Team at Work Third Edition Roger LeRoy Miller Institute for University Studies Mary Meinzinger Urisko Madonna University Prepared by Bradene L.
More informationSemi-supervised methods of text processing, and an application to medical concept extraction. Yacine Jernite Text-as-Data series September 17.
Semi-supervised methods of text processing, and an application to medical concept extraction Yacine Jernite Text-as-Data series September 17. 2015 What do we want from text? 1. Extract information 2. Link
More information(I couldn t find a Smartie Book) NEW Grade 5/6 Mathematics: (Number, Statistics and Probability) Title Smartie Mathematics
(I couldn t find a Smartie Book) NEW Grade 5/6 Mathematics: (Number, Statistics and Probability) Title Smartie Mathematics Lesson/ Unit Description Questions: How many Smarties are in a box? Is it the
More informationWhat's My Value? Using "Manipulatives" and Writing to Explain Place Value. by Amanda Donovan, 2016 CTI Fellow David Cox Road Elementary School
What's My Value? Using "Manipulatives" and Writing to Explain Place Value by Amanda Donovan, 2016 CTI Fellow David Cox Road Elementary School This curriculum unit is recommended for: Second and Third Grade
More informationCeramics 1 Course Summary Department: Visual Arts. Semester 1
Ceramics 1 Course Summary Department: Visual Arts Semester 1 Learning Objective #1 Learn ceramics vocabulary Target(s) and to Meet Learning Objective #1 Target 1: Expectation form reviewed Target 2: Discuss
More informationThe One Minute Preceptor: 5 Microskills for One-On-One Teaching
The One Minute Preceptor: 5 Microskills for One-On-One Teaching Acknowledgements This monograph was developed by the MAHEC Office of Regional Primary Care Education, Asheville, North Carolina. It was developed
More informationMODULE FRAMEWORK AND ASSESSMENT SHEET
MODULE FRAMEWORK AND ASSESSMENT SHEET LEARNING OUTCOMES (LOS) ASSESSMENT STANDARDS (ASS) FORMATIVE ASSESSMENT ASs Pages and (mark out of ) LOs (ave. out of ) SUMMATIVE ASSESSMENT Tasks or tests Ave for
More informationSTUDENT MOODLE ORIENTATION
BAKER UNIVERSITY SCHOOL OF PROFESSIONAL AND GRADUATE STUDIES STUDENT MOODLE ORIENTATION TABLE OF CONTENTS Introduction to Moodle... 2 Online Aptitude Assessment... 2 Moodle Icons... 6 Logging In... 8 Page
More informationBlinky Bill. Handwriting and. Alphabet Copy Book. Sample file. From Homeschooling Downunder. Manuscript Print Ball and Stick Font
Blinky Bill Handwriting and Alphabet Copy Book From Homeschooling Downunder Manuscript Print Ball and Stick Font Blinky Bill Handwriting & Alphabet Copy Book Manuscript Print (Ball and Stick) Illustrations
More information