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SUNY Suffolk - ECON - 111
Chapter 11 The Basics of Capital BudgetingShould we build this plant?111What is capital budgeting? Analysis of potential additions to fixed assets. Longterm decisions; involve large expenditures. Very important to firm's future.112Steps to Capital
SUNY Suffolk - ECON - 111
Chapter 14 Capital Structure and Leverage Business vs. Financial Risk Optimal Capital Structure Operating Leverage Capital Structure Theory141What is business risk?Uncertainty about future operating income (EBIT), i.e., how well can we predict operati
SUNY Suffolk - ECON - 111
Chapter Eleven Globalization and Economic SystemsImages of CapitalismImages of CommunismCapitalism or the market economyCapitalism is an economic system in which private individuals control the "factors of production". Workers decide for whom to work
SUNY Suffolk - ECON - 111
Chapter FiveGlobalization and TechnologyGlobalization and technology What is technology? What examples come to mind? Technology is claimed to be one of the four driver of modern globalization. How has it accelerated globalization?Modern technologyTh
SUNY Suffolk - ECON - 111
Chapter FourBusiness Culture, Ethics & Social ResponsibilityEthical issues in international businessBusiness ethics refers to accepted principles of right or wrong governing the conduct of business people Note: business ethics go beyond laws (why?)Et
SUNY Suffolk - ECON - 111
Chapter OneGlobalization: Past, Present and FutureGlobalization: Past, Present & FutureWhat is globalization? (What is "international business"?)The history of globalization Why globalization is controversialWhat is globalization?".A change in the
SUNY Suffolk - ECON - 111
Chapter SevenGlobalization and the Evolution of the Multinational Corporations (MNCs)Agenda What are multinational corporations (MNCs)? What drives a company to become an MNC? How do MNCs operate? What is their primary purpose? What are some of the is
SUNY Suffolk - ECON - 111
Chapter SixGlobalization and TradeTrade and globalizationWhy is trade important? How are globalization and trade related? Why do countries trade? What benefits do they hope to gain?The importance of trade and its relationship to globalizationDuring t
SUNY Suffolk - ECON - 111
Chapter ThreeGlobalization & Cultural ChangeCulture and globalizationSome key questions:Does globalization countries and peoples becoming evermore interconnected and interdependent inevitably result in an attack on culture and tradition? If so, to wha
SUNY Suffolk - ECON - 111
Chapter TwelveGlobalization and Political SystemsThere are three types of government. democracies, autocracies and theocraciesDemocraciesA system of government that represents the will of the governed ".by the people, for the people." qv. Abraham Lin
SUNY Suffolk - ECON - 111
Chapter TwoCulture"I want all the cultures of all lands to be blown about my house as freely as possible. but I refuse to be blown off my feet by any." Mahatma GandhiCulture: the basicsWhat is "culture" ? What does the word "culture" mean to you ? Wh
SUNY Suffolk - ECON - 111
LectureEntering Foreign MarketsBusinesses go overseas to seek1. New markets to grow their sales through geographic expansion 1. Resources to lower their costs (e.g. raw materials, manufacturing, labor, technology, etc.) 2. Knowledge and newly emerging
University of Iowa - ECON - 101
52:107 and 53:107 Sustainable Systems Assignment #2 Energy Audits Individual Assignment Due: February 6, 2012 Answer the following questions by typing your answers directly on this document file. Turn in your Word document assignment to your individual dr
DeVry Chicago - FINANCE - 515
Janice Fernandez Week 5 Project FI515 11-7 New Project Analysis You have been asked by the President of your company to evaluate the proposed acquisition of a new spectrometer for the firm's R&D department. The equipment's basic price is $70,000, and it w
RIT - CHEMISTRY - 205
Parts in Italics should be considered info and should not be transferred directly to your report. You can type right into the Tables and modify them if you want. Template for SCHC-205 Cut and Paste anything you wish and place into your report. Cover Page
Andhra University - CHEM - chem
Curriculum Bias DetectorsBias BustersBook Title/Author/Reference Information: Brief description of text:Type of Bias 1. Invisibility 2. Stereotyping 3. Imbalance and selectivity 4. Unreality 5. Fragmentation and isolation 6. Linguistic bias 7. Cosmetic
Andhra University - CHEM - chem
Effective SchoolsArtifact:Effective school observation Management and Motivation Examples Strong Leadership Clear School Mission Safe and Orderly Climate Monitoring of Student Progress High Expectations
Andhra University - CHEM - chem
Testing Do's and Don'tsWhat Teachers SHOULD Do . . . 1. 2. 3. What Teacher Should NOT Do . . . 1. 2. 3.
Andhra University - CHEM - chem
Support Staff InterviewDescribe your job duties.What do you like best about your job?What is the most challenging aspect of your job?How do students, teachers, and administrators impact your job?What is one thing about your job that you would like st
Andhra University - CHEM - chem
Developmental Areas and Socioeconomic ClassPhysical (such as size, shape, fitness, health, medical resources):Social (such as autonomy, civility, relationships):Emotional (such as expressiveness, empathy, motivation):Moral (such as ethics, honesty, go
Andhra University - CHEM - chem
Philosophy on the Big ScreenMovie: _ Major Education Philosophy Cinematic Evidence
Andhra University - CHEM - chem
Effective Lecture IdeasWhat pulled you into the lecture? (a great story? a provocative question?)How did you know where the lecture was going? (Was the purpose or objective stated or implied?)How did the speaker use presentation or communication skills
Andhra University - CHEM - chem
What You See and What You GetIndicators of Education PhilosophyCourse: _Room arrangement:Teacher-student interactions:Student-initiated actions:Instructional grouping and organization (full class, individuals/groups, centers/stations):Instructional
Andhra University - CHEM - chem
Assessing the AssessorWhat are the purposes behind these tests?Give examples of when these policies and procedures work effectively-and when there are problems.What is the best thing about these tests, and what is the worst?What is the biggest problem
Andhra University - CHEM - chem
Teacher FeedbackFeedbackPraiseNumber of ResponsesAcceptanceRemediationCriticism
Andhra University - CHEM - chem
Developmental and Psychosocial StagesStageEarly ChildhoodDescription and Evidence ObservedElementary and Middle School YearsAdolescence
Andhra University - CHEM - chem
Teaching Strategies Observation FormTeaching Style: Class:Examples:Assets:Downside:What appealed to you?What student learning style does this build on?
Andhra University - CHEM - chem
Rules, Rituals, and RoutinesArtifact: Management/Motivation Observation Examples
Andhra University - CHEM - chem
Class Comedy ClubThe funniest teacher I recall from school . . .The funniest student happening was . . .It sure was funny in school when . . . and she/he/they really did (or didn't) get in trouble . . .A teacher walks into a staff lounge . . .How man
Andhra University - CHEM - chem
Portfolio AssessmentNot at All Very 1 2 Item Purposeful: Selective: Diverse: Ongoing: Reflective: Collaborative: Other: _ Overall Appraisal: 3 4 Not Applicable 5
Andhra University - CHEM - chem
Website of the MonthResponsibilityNeed to Improve 1 2 3 Frequency of Updates Ease of Navigation Accuracy Clarity of Content Value of Content _ (Your criterion) _ (Your criterion) Overall Appraisal:Did This Well 4 5
Andhra University - CHEM - chem
Information Interview1. What is the school district looking for in a teachercandidate?2. Are there any special training or experience beyond state certification that the district looks for or requires?3. In what subject areas and at what grade levels d
Kaplan University - IT - MT300-01
MT300 Unit 6 Crossword1G L O B 2 A P 3 P L 6 E T L I N F O R M A T I O N 4 U A 9 T A B 12 E N 14 C O M P 21 V I L E R R C R Y P T I O N U A L 18 B R O A D B 27 B A C K W A N 28F I E L D 30 R R D C 26 C O O K I E A L L Y S I M P L E S Y M P A 23 G L E A L
DeVry Fresno - ACCOUNTING - ACC 504
Financial Statement Analysis ProjectFI504 Accounting and Finance: Managerial Use Prof: Diosdado BayangosWritten by:Rajashree Rudrapattana Lucy Hoang Jeff Ramos Victoria Berahmandpour Date: 06/12/2011Table of ContentsExecutive Summary.3 Brief Backgrou
Maryland - CLAS - 170
HERMES Hermes: son of Zeus and Maia, born on Earth in a cave and dwelled in the cave, represent a different socioeconomic voice the lower class, the poor (as opposed to aristocracy). Creation of Hermes shows an economic change in Greece. He is really obse
Maryland - CLAS - 170
Chapter 1:Zeus-the sky god name means bright or shining Mnemosyne-personification of memory Muses- Zeus 9 daughters that were the inspiration of Hesiod's Theogony and the patrons of literature and the fine arts Heinrich Schliemann-the person who found th
Maryland - CLAS - 170
Antigone ("Against Birth") Tragic heroine who isolates herself from womanhood and humanity altogether She rejected the socially correct role laid out for women. By rejected marriage and womanhood altogether, Antigone really is transforming to a masculine
University of Texas - GOV 310L - 310L
9/2/11--Constitutional amendments. Bill of rights comprises the first ten amendments to the Constitution. o Civil rights cannot be deprived of you o Civil Liberties are granted by the government and they can be restricted from you by the government o Y
University of Minnesota - CSCI - 5512
CSci 5512: Gibbs Sampling for Approximate Inference in Bayesian NetworksLet p(X1 , . . . , Xn |e1 , . . . , em ) denote the joint distribution of a set of random variables (X1 , . . . , Xn ) conditioned on a set of evidence variables (e1 , . . . , em ).
University of Minnesota - CSCI - 5512
CSCI 5512: Artificial Intelligence II (Spring11) Prof. Schrater Homework 1 (Due Wed, Jan 26, 4pm) 1. Consider the domain of dealing 5card poker hands from a standard deck of 52 cards under the assumption the dealer is fair. a. How many atomic events are
University of Minnesota - CSCI - 5512
CSCI 5512: Artificial Intelligence II (Spring'10) Homework 2 (Due Mar 17 by11:59 PM) 1. (20 points) Consider the Rain network in Figure 1. Assume that WetGrass = true. For simplicity, we denote the events by c, s, r and w for Cloudy=true, Sprinkler=tr
University of Minnesota - CSCI - 5512
Rational decisionsChapter 16Chapter 161Outline Rational preferences Utilities Money Multiattribute utilities Decision networks Value of informationChapter 162PreferencesAn agent chooses among prizes (A, B, etc.) and lotteries, i.e., situations wi
University of Minnesota - CSCI - 5512
Learning with Hidden VariablesCSci 5512: Artificial Intelligence IIHidden VariablesReal world problem have hidden variables No training data available on hidden variables Model cannot be built without training data Inference cannot be done without mode
University of Minnesota - CSCI - 5512
Temporal probability modelsChapter 15, Sections 15Chapter 15, Sections 151Outline Time and uncertainty Inference: ltering, prediction, smoothing Hidden Markov models Kalman lters (a brief mention) Dynamic Bayesian networks Particle lteringChapter 15
University of Minnesota - CSCI - 5512
University of Minnesota - CSCI - 5512
9 c A 9 c e 6 V 6 7 9 c e 6 V 7 6 6 9 e A 9 7 a GQ y s w C u t s E pQ E h V e e c E a Y R V T I R Q EI G E C A 9 7 @fbB@b8UibB@b8if8f@bb@Dw bbx a bFv8E rqHFigfbdPI b`XWUBSPHFDB@86A Gentle Tutorial of the EM Algorithm and its Application to Parameter Esti
University of Minnesota - CSCI - 5512
Markov ChainsTutorial #5 Ydo Wexler & Dan Geiger .Statistical Parameter EstimationReminder The basic paradigm:Data setModelParameters: MLE / bayesian approach Input data: series of observations X1, X2 . Xt We assumed observations were i.i.d (in
University of Minnesota - CSCI - 5512
Reinforcement LearningCSci 5512: Artificial Intelligence IIOutlineReinforcement Learning Passive Reinforcement Learning Active Reinforcement Learning Generalizations Policy SearchReinforcement Learning (RL)Learning what to do to maximize rewardLearn
University of Minnesota - CSCI - 5512
Artificial Intelligence IISpring 2011 Paul SchraterGeneral Information Course Number: CSci 5512 Class: M W 4:00-05:15 pm Web page: http:/www-users.itlabs.umn.edu/ classes/Spring- 2010/csci5512Or go to www.schrater.org Click on schrater's homepage Foll
University of Minnesota - CSCI - 5512
Markov Chain Monte Carlo and Gibbs SamplingLecture Notes for EEB 581, version 26 April 2004 c B. Walsh 2004A major limitation towards more widespread implementation of Bayesian approaches is that obtaining the posterior distribution often requires the i
University of Minnesota - CSCI - 5512
498IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 47, NO. 2, FEBRUARY 2001Factor Graphs and the Sum-Product AlgorithmFrank R. Kschischang, Senior Member, IEEE, Brendan J. Frey, Member, IEEE, and Hans-Andrea Loeliger, Member, IEEEAbstractAlgorithms that
University of Minnesota - CSCI - 5512
Machine Learning, 50, 543, 2003 c 2003 Kluwer Academic Publishers. Manufactured in The Netherlands.An Introduction to MCMC for Machine LearningCHRISTOPHE ANDRIEU C.Andrieu@bristol.ac.uk Department of Mathematics, Statistics Group, University of Bristol,
University of Minnesota - CSCI - 5512
ProblemsPrimarily of two types: Integration and Optimization Bayesian inference and learningComputing normalization in Bayesian methods p(y |x) = Marginalization: p(y |x) = Expectation: p(y )p(x|y ) p(y )p(x|y )dy p(y , z|x)dz f (y )p(y |x)dyy zEy |x
University of Minnesota - CSCI - 5512
Bayesian networks A simple, graphical notation for conditional independence assertions and hence for compact specification of full joint distributions SyntaxA set of nodes, one per variable A directed, acyclic graph (link implies direct influence) A
University of Minnesota - CSCI - 5512
M aki C om pl D eci ons ng ex siCSci 5512: Artificial Intelligence IISequential Decision ProblemsSearchexplicit actions and subgoals uncertainty and utilityPlanninguncertainty and utility explicit actions and subgoalsMarkov decision problems (MDPs)
University of Minnesota - CSCI - 5512
Exact InferenceCSci 5512: Artificial Intelligence II Instructor: Paul Schrater Overview: Inference Tasks Simple Queries: Compute posterior marginals P(b|j,m)Overview: Inference Tasks Simple Queries: Compute posterior marginals P(b|j,m) Conjuncti
University of Minnesota - CSCI - 5512
EM Demystified: An Expectation-Maximization TutorialYihua Chen and Maya R. Gupta Department of Electrical Engineering University of Washington Seattle, WA 98195 cfw_yhchen,gupta@ee.washington.eduUW UWUWEE Technical Report Number UWEETR-2010-0002Februa
University of Minnesota - CSCI - 5512
Sum-Product Algorithm CSci 5512: Artificial Intelligence II Factor Graphs Many problems deal with global function of many variables Global function "factors" into product of local functions Efficient algorithms take advantage of such factorization Fac
University of Minnesota - CSCI - 5512
CSCI 5512: Artificial Intelligence II Spring 2011 Homework #3, Due May 2nd 1. (20 points) This question considers the value of perfect information (VPI) VPI(Ej) which evaluates the value of additional information Ej given existing information E. (a) (8
University of Minnesota - CSCI - 5512
UncertaintyChapter 13Chapter 131Outline Uncertainty Probability Syntax and Semantics Inference Independence and Bayes' RuleChapter 132UncertaintyLet action At = leave for airport t minutes before flight Will At get me there on time? Problems: 1)
University of Minnesota - CSCI - 5512
Approximate Inference CSci 5512: Artificial Intelligence IIBayesian Networks with LoopsP(C) .50 Cloudy C T F P(S|C) .10 .5 Sprinkler Wet Grass S T T F F R T F T F P(W|S,R) .99 .90 .90 .01 Rain C T F P(R|C) .80 .20A direct application of sum-product c