# Register now to access 7 million high quality study materials (What's Course Hero?) Course Hero is the premier provider of high quality online educational resources. With millions of study documents, online tutors, digital flashcards and free courseware, Course Hero is helping students learn more efficiently and effectively. Whether you're interested in exploring new subjects or mastering key topics for your next exam, Course Hero has the tools you need to achieve your goals.

36 Pages

### scherrer07gmm

Course: MUMT 611, Fall 2009
School: McGill
Rating:

Word Count: 2406

#### Document Preview

Concepts Key Practical example of GMMs applied to MIR Other Applications Conclusion Gaussian Mixture Model Classiers Applications to MIR Bertrand SCHERRER February 7, 2007 Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion Outline 1 Key Concepts GMM : an Unsupervised Classier&quot; Why Gaussian Mixture&quot; ? Practical example...

Register Now

#### Unformatted Document Excerpt

Coursehero >> Canada >> McGill >> MUMT 611

Course Hero has millions of student submitted documents similar to the one
below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.

Course Hero has millions of student submitted documents similar to the one below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.
Concepts Key Practical example of GMMs applied to MIR Other Applications Conclusion Gaussian Mixture Model Classiers Applications to MIR Bertrand SCHERRER February 7, 2007 Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion Outline 1 Key Concepts GMM : an Unsupervised Classier" Why Gaussian Mixture" ? Practical example of GMMs applied to MIR Context GMM Training Classication test Other Applications Musical Instrument Identication in Polyphonic Music Extraction of melodic lines from audio recordings Conclusion Bertrand SCHERRER GMM Classiers in MIR 2 3 4 Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion GMM : an Unsupervised Classier" Why Gaussian Mixture" ? Outline 1 Key Concepts GMM : an Unsupervised Classier" Why Gaussian Mixture" ? Practical example of GMMs applied to MIR Context GMM Training Classication test Other Applications Musical Instrument Identication in Polyphonic Music Extraction of melodic lines from audio recordings Conclusion Bertrand SCHERRER GMM Classiers in MIR 2 3 4 Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion GMM : an Unsupervised Classier" Why Gaussian Mixture" ? GMM : an Unsupervised Classier" Unsupervised Classier The training samples of the classier are not labelled to show their category membership [Duda 73]. Advantages Less time consuming when applied to a large set of data. Ability to track (slow) time-evolving patterns. Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion GMM : an Unsupervised Classier" Why Gaussian Mixture" ? GMM : an Unsupervised Classier" Unsupervised Classier The training samples of the classier are not labelled to show their category membership [Duda 73]. Advantages Less time consuming when applied to a large set of data. Ability to track (slow) time-evolving patterns. Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion GMM : an Unsupervised Classier" Why Gaussian Mixture" ? Why Gaussian Mixture" ? In GMM classier, for a given class, the probability density function of the observation vector is modelled as : K p(x|Ci ) = k =1 P(k |Ci ).Gk (k , k ) (1) where : x is a d-component feature vector. k s are the d-component mean vectors of Gaussian Gk . k s are the d-by-d covariance matrices of Gaussian Gk . P(k |Ci ) is the a priori probability of Gaussian Gk for instrument class Ci . Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion GMM : an Unsupervised Classier" Why Gaussian Mixture" ? Why Gaussian Mixture" ? In GMM classier, for a given class, the probability density function of the observation vector is modelled as : K p(x|Ci ) = k =1 P(k |Ci ).Gk (k , k ) (1) where : x is a d-component feature vector. k s are the d-component mean vectors of Gaussian Gk . k s are the d-by-d covariance matrices of Gaussian Gk . P(k |Ci ) is the a priori probability of Gaussian Gk for instrument class Ci . Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion GMM : an Unsupervised Classier" Why Gaussian Mixture" ? Why Gaussian Mixture" ? In GMM classier, for a given class, the probability density function of the observation vector is modelled as : K p(x|Ci ) = k =1 P(k |Ci ).Gk (k , k ) (1) where : x is a d-component feature vector. k s are the d-component mean vectors of Gaussian Gk . k s are the d-by-d covariance matrices of Gaussian Gk . P(k |Ci ) is the a priori probability of Gaussian Gk for instrument class Ci . Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion GMM : an Unsupervised Classier" Why Gaussian Mixture" ? Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion Context GMM Training Classication test Outline 1 Key Concepts GMM : an Unsupervised Classier" Why Gaussian Mixture" ? Practical example of GMMs applied to MIR Context GMM Training Classication test Other Applications Musical Instrument Identication in Polyphonic Music Extraction of melodic lines from audio recordings Conclusion Bertrand SCHERRER GMM Classiers in MIR 2 3 4 Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion Context GMM Training Classication test GMMs are used in many different elds. Look at one clear example of GMM classication applied to MIR: [Marques 99] Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion Context GMM Training Classication test GMMs are used in many different elds. Look at one clear example of GMM classication applied to MIR: [Marques 99] Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion Context GMM Training Classication test Context Objectives Instrument identication in monophonic music: 8 different classes": bagpipe, clarinet, ute, harpsichord, organ, piano, trombone and violin . on very short recordings (0.2s). What is to be classied ? A set X of m unlabelled observations (cepstral, mel-cepstral and LPC coefcients) : X = [x1 , x2 . . . xm ]. Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion Context GMM Training Classication test Context Objectives Instrument identication in monophonic music: 8 different classes": bagpipe, clarinet, ute, harpsichord, organ, piano, trombone and violin . on very short recordings (0.2s). What is to be classied ? A set X of m unlabelled observations (cepstral, mel-cepstral and LPC coefcients) : X = [x1 , x2 . . . xm ]. Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion Context GMM Training Classication test Context Assuming that the observations are i.i.d., the likelihood that the entire set of observations X has been produced by a violin ( C0 for example) is : m p (X|C0 ) = t=1 p(xt |C0 ) (2) and each p(xt |C0 ) is modelled as a mixture of K multivariate gaussians. Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion Context GMM Training Classication test Objectives of the GMM training At this stage, one tries to estimate, for all the classes of instruments, the parameters of the GMM: ik = [P(k |Ci ), k ,i , k ,i ] for k = 1 . . . K . Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion Context GMM Training Classication test How to estimate the GMM parameters : MLE ? Ideal way would be to use the Maximum Likelihood Estimation (a.k.a. MLE). MLE theoretically consists in nding = [ i1 , i2 . . . iK ], maximizing p(X|Ci ). In the case where all the parameters are unknown, MLE becomes very complex ... and unreliable need for an alternate method. Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion Context GMM Training Classication test How to estimate the GMM parameters : MLE ? Ideal way would be to use the Maximum Likelihood Estimation (a.k.a. MLE). MLE theoretically consists in nding = [ i1 , i2 . . . iK ], maximizing p(X|Ci ). In the case where all the parameters are unknown, MLE becomes very complex ... and unreliable need for an alternate method. Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs to applied MIR Other Applications Conclusion Context GMM Training Classication test How to estimate the GMM parameters : MLE ? Ideal way would be to use the Maximum Likelihood Estimation (a.k.a. MLE). MLE theoretically consists in nding = [ i1 , i2 . . . iK ], maximizing p(X|Ci ). In the case where all the parameters are unknown, MLE becomes very complex ... and unreliable need for an alternate method. Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion Context GMM Training Classication test How to estimate the GMM parameters : MLE ? Ideal way would be to use the Maximum Likelihood Estimation (a.k.a. MLE). MLE theoretically consists in nding = [ i1 , i2 . . . iK ], maximizing p(X|Ci ). In the case where all the parameters are unknown, MLE becomes very complex ... and unreliable need for an alternate method. Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion Context GMM Training Classication test How to estimate the GMM parameters : EM Expectation Maximisation algorithm [Dempster 77] is an iterative solution very often used for MLE. Initial Steps 1 Compute closed-form expressions of the parameters of the GMM corresponding to a local extremum of the likelihood p(X|(t)). Make a rst guess on the values of (t). 2 Lets iterate ! Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion Context GMM Training Classication test How to estimate the GMM parameters : EM Expectation Maximisation algorithm [Dempster 77] is an iterative solution very often used for MLE. Initial Steps 1 Compute closed-form expressions of the parameters of the GMM corresponding to a local extremum of the likelihood p(X|(t)). Make a rst guess on the values of (t). 2 Lets iterate ! Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion Context GMM Training Classication test How to estimate the GMM parameters : EM Expectation Maximisation algorithm [Dempster 77] is an iterative solution very often used for MLE. Initial Steps 1 Compute closed-form expressions of the parameters of the GMM corresponding to a local extremum of the likelihood p(X|(t)). Make a rst guess on the values of (t). 2 Lets iterate ! Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion Context GMM Training Classication test Classication Test A feature vector xt is said to belong to an instrument class i if it maximizes p(Ci |xt ) = p(xt |Ci ).p(Ci ). If classes can occur with the same probability, since we know for all classes, xt belongs to the class for which p(xt |Ci ) is maximum. Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion Context GMM Training Classication test In [Marques 99]: Features : mel cepstral feature vectors (16-element vectors). Order of the GMM: 2. overall error rate of 37%. Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion Musical Instrument Identication in Polyphonic Music Extraction of melodic lines from audio recordings Outline 1 Key Concepts GMM : an Unsupervised Classier" Why Gaussian Mixture" ? Practical example of GMMs applied to MIR Context GMM Training Classication test Other Applications Musical Instrument Identication in Polyphonic Music Extraction of melodic lines from audio recordings Conclusion Bertrand SCHERRER GMM Classiers in MIR 2 3 4 Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion Musical Instrument Identication in Polyphonic Music Extraction of melodic lines from audio recordings Musical Instrument Identication in Polyphonic Music In [Eggink 03], try to recognize 2 instruments playing at the same time. Approach based on estimation of multiple fundamental frequencies to sort features fed into the GMM. Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion Musical Instrument Identication in Polyphonic Music Extraction of melodic lines from audio recordings Figure 1 [Eggink 03] Bertrand SCHERRER GMM Classiers in MIR Key Concepts Practical example of GMMs applied to MIR Other Applications Conclusion Musical Instrument Identication in Polyphonic Music Extraction of melodic lines from audio recordings Features : cepstral coefcients clearly belonging to different tones. Order of the GMM: 120. Training Material: monophonic recordings of tones or melodies ...

Find millions of documents on Course Hero - Study Guides, Lecture Notes, Reference Materials, Practice Exams and more. Course Hero has millions of course specific materials providing students with the best way to expand their education.

Below is a small sample set of documents:

Maryland - GLUE - 878
The focus of our discussions in Week 5 will be on how users formulatequeries and on how machines make use of those queries.Here are some questions to help guide your reading for week 5:1) Taylor observes that users must compromise their informat
Maryland - GLUE - 878
Searching interaction:Facets for eliciting user needs User enters subject field of search. System displays list of facets (limiting aspects). User indicates first aspect for limiting the searchSubject field of search:EducationIndicate limiting
Maryland - GLUE - 878
LBSC 878 Oard/Soergel Dagobert Soergel ds52@umail.umd.edu February 26, 1999An outline of issues in feature assignment (aka indexing)This outline presents an overall view of the twin problems of feature assignment and matching.General framework:
Maryland - GLUE - 878
878 Spring 1999 Oard/SoergelFeb. 1, 1999Outline for the discussion of relevance1 Definition of relevance/usefulness Very broad (almost tautological) definition for any or any type of entity An entity is relevant for a user if it serves the users
Maryland - GLUE - 878
You should be prepared to make brief comments on the following threequestions. Thinking about these will be useful for your papers in any event.1 What are the functions of classification in the context of the system ortopic you discuss in your
Maryland - GLUE - 878
Here are some questions to consider while preparing for week 6 of LBSC878:1) The central focus of this week's readings is the ranked retrievalparadigm in which users are presented with a list of documents that (hopefully) has the best documents
Maryland - GLUE - 878
LBSC 878 Oard/SoergelSpring 1999Knowledge representation 2Application of KR concepts to analysis of the readings and to focus areasKnowledge representation concepts summary Approaches to knowledge representation Entity-relationship approach Se
Maryland - GLUE - 878
Designing a Collaborative Filtering System-&lt;Description&gt;Collaborative filtering systems assist and augment the natural process ofrelying on friends, colleagues, publications, and other sources tomake the choices that arise in eve
Maryland - GLUE - 878
Designing a Recommender SystemJinmook Kim LBSC 878: Information Storage and Retrieval College of Library &amp; Information Services Week 11: April 19, 1999April 19, 19991Agenda Terminology Implicit Feedback Implications of Relevance on Filter
Maryland - GLUE - 878
LBSC 878 Oard/Soergel Dagobert Soergel ds52@umail.umd.edu March 7, 1999Week 7. March 15, 1999Source selection and item selectionPreliminary outline and notes on readingsSource selectionBrief overview in the lecture. Buckland gives some backgr
Brookdale - ENGM - 2262
Part IIVectors, Matrices, and Vector Calculus71. (a) 6i + 12j 2. (a) 3, 3 3. (a) 12, 0 4. (a)1 2iVectorsEXERCISES 7.1Vectors in 2-Space(b) i + 8j (b) 3, 4 (b) 4, 5 (b)2 3i(c) 3i (c) 1,2 (c) 4, 5 (c) 1 i j 3 (c) 3i 5j (c) 6, 18 (c)
Caltech - T - 970130
LASER INTERFEROMETER GRAVITATIONAL WAVE OBSERVATORY - LIGO CALIFORNIA INSTITUTE OF TECHNOLOGY MASSACHUSETTS INSTITUTE OF TECHNOLOGY &amp;VIRGO EXPERIMENTCNRS-INFNDocument Type LIGO-T970130-G-E: September 7, Technical Note VIRGO-SPE-LAP-5400-102 200
Allan Hancock College - PAGE - 104034
Archives and RecordsIMpInformation Management ressCONTENTSWelcome to IMpress Pg1 December, 2006 Issue 1Information ManagementWelcome!Welcome to the first edition of IMpress, Archives and Records quarterly newsletter. Each edition will be fi
Allan Hancock College - PAGE - 89084
The 93rd Annual AMEB AwardsThe Octagon Theatre The University of Western Australia Tuesday 11 March 2008 7 pmMaster of Ceremonies:Mrs Karen Goddard, BEd, DipPE, LSDAThe AMEB gratefully acknowledges support from: Zenith Music The WA Music Teacher
Willamette - CS - 445
December 15, 2006Name _CS445 Final ExamFall 20061. (max = 14) 7. 2. (max = 10) 8. 3. (max = 17) 9. Final Score _(max=95)(max = 25) (max = 9) (max = 20)1. (7 pts each, 14 pts total) 3D Transforms in homogeneous coordinates: Write down the 4
Willamette - CS - 445
October 18, 2006Name _CS445 Exam 1Fall 20061. (max = 10) 5. 2. (max = 24) 6. 3. (max = 10) 7. 4. (max = 18) 8. Final Score _(max=100)(max = 6) (max = 10) (max = 12) (max = 10)1) (10 pts) Discuss the meaning of and motivation for homogeneou
Texas El Paso - ACADEMICS - 367
CURRICULUM VITAE ARTHUR H. HARRISAddress:Laboratory for Environmental Biology, Centennial Museum, University of Texas at El Paso, El Paso, TX 79968-0915Home: 2201 N. Campbell St. El Paso, TX 79902-3201 Business Phone: (915) 747-6985, 747-6835 C
Oregon State - BA - 444
1)First, Put = Call + Xe-rt S = 0.85 + 35 e-.045(64/365) 32.05 = \$3.52If P = 32.05, then d1 = ln[32.05/35] + (.045 + (.342/2)(64/365) -.34 * SQRT(64/365) Which is approximately -0.5 Looking at a Cumulative Normal Table incremented by .025s give
Penn State - BCF - 134
Characterizing neurocranial shape in microcephalic children. B.C. Frazier, K.E. Willmore, J.T. Richtsmeier. Department of Anthropology, Pennsylvania State University. Microcephaly has come to the forefront of discussion in physical anthropology in li
Penn State - HSA - 109
A MANUAL FOR THE ASSESSMENT OF HISTORIC LOAD-BEARING MASONRY STRUCTURES Thomas E. Boothby 1 and H. Sezer Atamturktur 2Abstract The assessment of unreinforced masonry structures, especially in arched or vaulted forms, is difficult to undertake in pr
Allan Hancock College - COMP - 704
Chapter 4Software Processescomp284-Software Engineering1ObjectivesTo introduce software process and software process models. To describe three generic process models and when they may be used. To outline process models for requirements engi
Allan Hancock College - COMP - 704
%!PS-Adobe-2.0 %Creator: dvips(k) 5.95a Copyright 2005 Radical Eye Software %Title: slides.dvi %Pages: 66 %PageOrder: Ascend %Orientation: Landscape %BoundingBox: 0 0 595 842 %DocumentFonts: CMBX12 CMR12 CMR5 CMR10 CMBX10 CMSY10 CMSL10 CMTT10 %+ CMTI
Iowa State - AE - 568
AE 568X Pretreatment of biomassSpring 2009 Lectures: 2 hours (T&amp;TH 10:00~10:50, 115 Davidson) Lab: 2 hours (T 1:10~3:00, 3232 NSRIC) Instructor: Tae Hyun Kim; Agricultural and Biosystems Engineering 3101 NSRIC, Phone: 515-294-7136 Email: thkim@iasta
Iowa State - AE - 568
Instructor:Tae Hyun Kim (3101 NSRIC) Phone: 515-294-7136, Email: thkim@iastate.eduA E 568X Pretreatment of BiomassHomework #2. Reading (Due; 1/29) Find a DOE report (DOE/SC-0095 Breaking the Biological Barriers to Cellulosic Ethanol.pdf. Read pp
Iowa State - AE - 568
Instructor:Tae Hyun Kim (3101 NSRIC) Phone: 515-294-7136, Email: thkim@iastate.eduA E 568X Pretreatment of BiomassHomework #7. Reading (Due; 3/10) Find a DOE report (DOE/SC-0095 Breaking the Biological Barriers to Cellulosic Ethanol.pdf. Read pp
Iowa State - AE - 568
AE 568 Pretreatment of biomassSpring 2009 Lectures: 2 hours Lab: 2 hours Instructor: Tae Hyun Kim; Agricultural and Biosystems Engineering 3101 NSRIC, Phone: 5152947136 Email: thkim@iastate.edu A 3credit course to discuss the brief organic chemistry
Penn State - IE - 553
Penn State - BJC - 191
Penn State - BWF - 114
Table of Contents1 Abstract 2 Executive Summary 3 Introduction 4 Project Background 4 6 7 9 10 11 12 13 13 16 16 18 18 19 20 20 22 24 26 27 28 28 30 34 35 38 Project Statistics &amp; Architecture Building Systems Design Coordination Local Conditions Tem
Penn State - BWP - 113
Brad Pietropola Construction Management Option Resource Center, Holy Redeemer Hospital Meadowbrook, Pennsylvania Faculty Consultant: D RileyTable of ContentsExecutive Summary Site Layout Planning Temporary Utilities General Conditions Project Sche
Penn State - DWF - 137
Technical Assignment #2 Construction Management Dave FoxWrangle Hill Elementary School Advisor: Dr. Riley 11/2/2007David Fox Dr. David Riley 11/2/2007Wrangle Hill Elementary School New Castle, DE Technical Assignment 2Table of ContentsExecut
NYU - AS - 4505
New York University Department of Spanish and PortugueseMinor in Spanish or PortugueseNumber of Required Courses (all conducted in Spanish or Portuguese): . 5 Recommended Breakdown of Minor REQUIRED COURSES (2) Advanced Languages (1 course): Advan
Penn State - AMT - 903
Aaron TroutSenior Thesis 2005Construction ManagementAnalysis 1 4D Coordination ModelDescription of 4D ModelingThe traditional means of design and construction planning consist of 2D drawings and network diagrams. These tools are still widely
Penn State - AMT - 903
Aaron TroutSenior Thesis 2005Construction ManagementAlternate System and Methods AnalysisSite Layout PlanningDescription of Key Features *Note these site plans can be found in Appendix B Excavation Site Plan: For the excavation of the LSM bui
Penn State - PAR - 117
PiiLA DocumentationPiiLA (pronounced pie-la) is an Excel spreadsheet with embedded macros designed to aid in the process of reviewing the log files generated by the Proventsure personal identifiable information (PII) scanning software used at Penn S
Concordia Chicago - RHUDSON - 356
EE 356 Population Genetics - I (Winter, 2007) Instructors: 7 lectures Richard R. Hudson (rr-hudson @uchicago .edu) 11 lectures - Chung-I Wu (ci wu @u chicago.edu) TA: Adi Alon ( adia@uchicago .edu ) Course: ECEV 35600 01 Title: Population Gen etics1
W. Alabama - ECE - 750
DSL Implementation in MetaOCaml, Template Haskell, and C+Krzysztof Czarnecki1 , John ODonnell2 , Jrg Striegnitz3 , and Walid Taha4 o2University of Waterloo, Canada University of Glasgow, United Kingdom 3 Research Centre Jlich, Germany u 4 Rice Un
Toledo - CHM - 346
Nitrogen Heterocycles: From Natural Product Inspired Methods to Peptide-Heterocycle ConjugatesRobert Batey, Julia Gavrilyuk, Ghotas Evindar, David PowellDepartment of Chemistry University of Toronto 80 St. George Street Toronto, Ontario, M5S 3H6 CA
Allan Hancock College - ARTS - 157411
Limina, Volume 14, 2008Brian WinkenwederThe Newspaper as Nationalist Icon, or How to Paint Imagined CommunitiesBrian WinkenwederLinfield CollegeThrough a careful examination of the conditions under which Sir David Wilkie painted and exhibited
Texas El Paso - ACADEMICS - 990
Department: Civil Engineering Number: CE 4335 Title: Structural Design I Catalog Description: Reinforced concrete theory, design of beams, columns, slabs, footings, and retaining walls using current design specifications. Prerequisites: CE 3343 Textb
Monroe CC - HUM - 106
Monroe CC - HUM - 106
WHO AM I AS A PERSON?(taken from Along the Way: A Counselor Self-Assessment, pg. 111)1. How do I assess my developmental history up to this point of my life? What were the high and low points? 2. When did I realize I was an adult? How did I handle
Monroe CC - HUM - 106
FIELDWORK LOG Date _ Student _ HUM 106C61EVENTASSESSMENTINTERVENTIONPERFORMANCE EVALUATIONHUM 106 106 fwlog example
Monroe CC - HUM - 106
ExampleFIELDWORK LOG Date May 30, 2006 Jane Doe HUM 106C61StudentEVENT Today at the day care center where I do my fieldwork, I observed two 4-year-olds, Billy and Jimmy, shoving one another. I called to them to stop and Billy, who is bigger tha
Monroe CC - HUM - 106
WHO AM I AS A PROFESSIONAL(taken from Along the Way: A Counselor Self-Assessment, pg. 111-112)1. What are my reasons for becoming a counselor? 2. Do I feel that my emotional issues will be addressed and resolved by becoming a counselor? 3. What is
Monroe CC - HUM - 106
Monroe CC - HUM - 106
OUTLINE FOR ORAL PRESENTATIONS I. Introduction A. Historical Background II. Key Concepts A. View of Human Nature B. Basic Characteristics III. The Therapeutic Process IV. Application: Therapeutic Techniques and Procedures A. Areas of Application V. S
Monroe CC - HUM - 106
communication leadsTo understand another persons feelings and experiences we need to attempt to enter his phenomenal field, his personal frame of reference through which he interacts with his world. However, since it is impossible for us to be the o
Penn State - NJS - 5041
NICHOLAS J. SMITHnjs5041@psu.edu 271 WALNUT ST. LUZERNE, PA 18709 PHONE: (570)288-4525 340 E. BEAVER AVE. APT.205 STATE COLLEGE, PA 16801 CELL: (207)651-8117OBJECTIVE To obtain an internship in the field of Actuarial Science EDUCATION Penn State S
NYU - MRG - 217
The Modifying Eect of Electoral Institutionsby Matthew Richard Golder Advisor: William Roberts Clark ABSTRACT This dissertation is an empirical study of the interaction between voter preferences, electoral institutions, and party systems. Unlike th
Eastern Oregon - HUM - 110
Oklahoma State - FP - 4213
ECEN4213Lab 1Computer Based System DesignECEN 4213 Computer Based System Design Lab 1: Introduction to the BASIC Stamp EditorEx # Max Points Points EarnedBonus pointsGrading criteria(1.0) _Instructor Initial15Program entered correc
Oklahoma State - FP - 4213
ECEN4213Lab 2Computer Based System DesignECEN 4213 Computer Based System Design Lab 2: Introduction to Microcontroller Programming and Switch InputEx # Max Points Points Earned Grading criteriaCircuit is wired correctly. (1.0) _Instructor In
Oklahoma State - FP - 4213
ECEN 4213Lab 3Computer Based System DesignNAME:_ECEN 4213 Computer Based System Design Lab 3: Analog InputsEx # Max Points Points Earned Grading criteriaCircuit is wired correctly. (3.0) _Instructor Initial16Program entered correctly
Oklahoma State - FP - 4213
ECEN 4213Lab 4Computer Based System DesignNAME:_ECEN 4213 Computer Based System Design Lab 4: Analog and DigitalEx # Max Points Points Earned Grading criteriaCircuit is wired correctly. (1.0) _Instructor Initial13Program entered corr
Oklahoma State - FP - 5263
Oklahoma State - FP - 5263
Oklahoma State - FP - 5263
Allan Hancock College - COMP - 170
comp170/570 UNIX/Linux Programming Environment Trial Exam 2nd semester 2005 Marks total 60 Time allowed: 2 hours. Calculators are not allowed. No notes.1Question 1. (6+3=9 marks) a) Explain what each of these UNIX commands do: i) ls a1*.* ii) gre
Oklahoma State - FP - 5263
Allan Hancock College - COMP - 170
COMP170 Semester 1 2007comp170 UNIX/Linux Programming Environment Exam semester 1, 2007 Marks total 65 Time allowed: 2 hours. Calculators are not allowed. No notes.1COMP170 Semester 1 2007Question 1. (6+4=10 marks) a) Explain what each of the