5 Pages

MSci331_Winter 2012 Syllabus (1)

Course: ECE 111, Spring 2012
School: Waterloo
Rating:
 
 
 
 
 

Word Count: 2402

Document Preview

:DR.AMEROBEIDI OFFICE UNIVERSITYOFWATERLOO DEPARTMENTOFMANAGEMENTSCIENCES IntroductiontoOptimization MSCI331 INSTRUCTOR :CPH3627 PHONE :X38505(ONLYINDIREEMERGENCIES) EMAIL :AAOBEIDI@ENGMAIL.UWATERLOO.CA OFFICEHOURS :MONDAY1:30PM2:30PM. Winter2012 COURSEDESCRIPTION This introductory course to optimization uses quantitative approaches to decision making and problem solving involving mathematical modeling and...

Register Now

Unformatted Document Excerpt

Coursehero >> Canada >> Waterloo >> ECE 111

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.
:DR.AMEROBEIDI OFFICE UNIVERSITYOFWATERLOO DEPARTMENTOFMANAGEMENTSCIENCES IntroductiontoOptimization MSCI331 INSTRUCTOR :CPH3627 PHONE :X38505(ONLYINDIREEMERGENCIES) EMAIL :AAOBEIDI@ENGMAIL.UWATERLOO.CA OFFICEHOURS :MONDAY1:30PM2:30PM. Winter2012 COURSEDESCRIPTION This introductory course to optimization uses quantitative approaches to decision making and problem solving involving mathematical modeling and formulations, solution methods, and output analysis. Students are introduced to a variety of practical problem formulations in management and engineering, and a number of solution methods, including, but not limited to linear optimization, network models, project management, and decision analysis. Students are also involved in group projects of reallife applications, where they go through conceptual and operational model design, analytical solution, output analysis, and recommendation. COURSEOBJECTIVES The emphasis in this course will be on the systematic structuring of the main characteristics of a reallife problem using mathematical models to explore a range of scenarios and determine which decisions are robust under a number of assumptions. Hence, our focus will be on developing your modeling skills, carrying out sensitivity analysis (Whatif), interpretation and analysis of output results, and computer implementations of operations research (OR) techniques using some software packages such as LINDO, LINGO, and GAMS. The use of quantitative modeling in different settings and hierarchical levels of decisionmaking will be illustrated through the modeling and analysis of realistic case studies. Upon a successful completion of the course, each student shouldbeableto: Analyzeaproblemsituationinengineering,management,orbusinessenvironment,focusingonimportantdetails. Buildingrelevantmodelsthatprovidevaluableinsightsforoperationaland/orstrategicdecisionmaking. Userelevantcomputerapplicationsoftwaretosolveandunderstandanoperationsresearchmodel. Appreciatetheadvantagesandlimitationsofoperationsresearchinaddressingreallifesituations. Becomeanactiveandcriticalmodelerratherthanapassiveconsumerofanalyticalwork. Learnkeycommunicationskillsofreportwritinganddevelopindependentlearningskills. CLASSSCHEDULE Lecture Tuesday 8:309:20AM Thursday 8:3010:20AM E21303A Wednesday 4:305:20PM Tutorial E21303A E21303A OFFICEHOURS Mondayfrom1:30PMto2:30PM.Otherdaysortimes,suchasTuesdayorFriday,canalsobearrangedbyappointment. TEACHINGASSISTANT TarekAbdallah,t3abdall@uwaterloo.ca.OfficeCPH4328.Officehour:TBD SUGGESTEDTEXTBOOK Wayne L. Winston and Munirpallam Venkataramanan, Introduction to Mathematical Programming: Applications and Algorithms: Volume1,4thEdition.DuxburyPress/CengageLearning,2003.ISBN13:9780534359645(availableatUWBookstore).Twocopies ofthetextbookareavailableatUWLibraryatDavisCentre.Bothcopiesareonathreehourreserve. In addition to the textbook, I encourage you to search and read articles from a number of relevant journals that contain the latest informationregardingthetheoryandpracticeofproblemsolvingandoperationsresearch(OR)ofreallifeapplications.Inparticular, the JanuaryFebruary issue of INTERFACES contains awardwinning articles of OR applications, while ORMS TODAY magazine provides a comprehensive look at OR and management science through stories, feature articles, case studies, software reviews and surveys. ALTERNATIVEBOOKS MSCI331IntroductiontoOptimization A.Obeidi Youalsomaywishtouseanalternativebookincaseyoucannotbuytherequiredone.IwouldsuggestthepopularbookbyHillier,F. and Lieberman, G., Introduction to Operations Research, but you are free to choose any other introductory to operations Research book. SOFTWARE A wide range of OR software packages are available for solving linear and integer programming models. XpressOptimizer is a good optimization software is designed for solving linear, integer and mixed integer programming problems. You can download and use XpressMP Student Edition, which is free, from Dash Optimization (http://optimization.fico.com/studentversionoffico xpress.html). Another good software to use is LPSolve which is free, open source, mixed integer program solver. You can also use LINDO,LINGO,LPSolve,GAMS,ortheoptimizationtoolboxinMATLABforsolvingawidevarietyofORmodels.Acomprehensivelist ofLPsoftwarepackagescanbefoundathttp://www.lionhrtpub.com/orms/surveys/LP/LPsurvey.html. SUMMARYOFTOPICS1 Linearprogrammingmodelformulationandsolution. LPsensitivityanalysis. LPdualityanalysis. Integerprogrammingmodelformulationandsolution. Networkmodels. Decisionanalysis. Projectpresentations. COURSEMANAGEMENT The course will be administered through the learning management system Desire2Learn. You are expected to use MSCI 331Winter 2012onWaterlooLearn(http://learn.uwaterloo.ca)websiteregularlyforallcourserelatedcorrespondenceandannouncements. Summaryofsomeconceptsandstudentsgradeswillalsobepostedinthecoursewebsite. EVALUATION The course grade will be based on a midterm examination, two homework assignments, three quizzes, a team project, and a final examination. If you miss an examination due to a VERIFIED extenuating circumstance,2 you would be offered an opportunity for a makeupexam.Thebreakdownofthemarkingschemeisasfollows: Homeworkassignment(2) 10 % Quizzes(3) 15% TeamProject 15 % MidtermExamination 20% FinalExamination 40 % EXAMINATIONS Allmidtermandfinalexaminationswillbeclosedbookandnotes.Youarepermittedtobringyourownhandwritten,onepage,two sided 8 x 11 crib sheet. Make sure to prepare an efficient and well organized sheet. The midterm exam will be on February 15, 2012 from 4:306:00 PM. Please follow all future updates for any changes. As for the final examination, the day, time, and location will be determined by the Office of the Registrar. So you should start checking 1 2 Theindicatednumbersofweeksareapproximate. Extenuating circumstance is an unforeseeable and beyond your control situation, which either prevents you from taking an examination or submitting a coursework or which affects your academic performance in the course. Extenuating circumstances will usually be health related or of apersonalnaturesuchasaccident,bereavementorotherpersonalissues.Atanyrateaverificationletter(VIForcounsellingletter)isrequired. Page2of5 MSCI331IntroductiontoOptimization A.Obeidi http://www.registrar.uwaterloo.ca/exams/finalexams.html by early March. If you require special accommodation for religious or cultural observances please notify me in writing by the second week of the term. Notice that Student travel plans are NOT consideredacceptablegroundsforgrantinganalternativeexaminationtime. COURSEPROJECT Thegoaloftheprojectistopresentstudentswithreallifeapplicationsofthemodelsandsolutionmethodsdiscussedinthecourse. Theprojectwillbedoneinteamsofthreestudents.RefertotheProjectInformationdocumentformoreinformation. HOMEWORKANDQUIZZES There will be two written homework assignments; both count 10% towards your final grade. Each assignment will cover topics that share compatible conceptual framework. The main objective of the assignments is to evaluate how students systematically analyze and solve problems using the appropriate mathematical modeling technique. Another objective is to assess the accuracy and correctness of the applied technique in solving problems. It is important that you demonstrate a thorough understanding of the differentapproachesandproceduresaswellasaccuracyinyouranswers;ifnot,expectsomedeductionofmarks. Solutions for the assignments will be posted AFTER ALL students submit their It works. is crucial that you work independently on assignments. Sharing ideas and solutions with a classmate or from the Internet constitutes an academic offence (see Academic Integritybelow).Failuretosubmitanassignmentwillresultinagradeof0%onthatcomponent;nomakeupwillbeoffered. Moreover, three quizzes will be given during tutorial times (specific dates are in the table below). Do not miss any of these quizzes, so schedule your calendar accordingly. Marked quizzes will be returned back to students during tutorial times or can be picked up fromtheTAsoffice.Unclaimedstudentsubmissionswillbekeptforthedurationofthetermafterthattimethematerialinquestion willbesecurelydestroyed.Finalexaminationswillbekeptforthedurationofoneyear. Date January18 February1 March14 LATEASSIGNMENTPOLICY All assignments and reports must be submitted on time using the appropriate dropbox located in the second floor of CPH building. If you know that you are going to be late and have an extenuating circumstance, you must contact the TA to explain your situation and arrange for a new submission time. Otherwise, a late assignment will be accepted but will incur a penalty. Work that is submitted late during the first two days without an extension granted will be penalized 25% of the assignment mark per calendar day or part of a day (including weekends and vacations). After two days, the work will not be accepted and it will be given a zero mark.Submittingalateassignmentmustnotaffectthesubmissiondateofsubsequentones. CLASSATTENDANCE Studentsareexpectedtoattendallclassesandtutorials(ontime)andwillbeheldresponsibleforallmaterialdiscussedineachclass regardlessofwhetherthestudentactuallyattendedclass. PERSONALCOMMUNICATIONANDCOMPUTERDEVICES Personal Communication and Computer devices such as cell phones, digital recorders, iPods, MP3 players, cameras or laptops are nottobeusedduringclassunlessIauthorizetheirusageforaclassrelatedpurpose.Allcellphonesaretobeturnedoffandarenot to be used during a class. If I give permission for the use of a personal computer for notetaking that is the sole purpose to which thesedevicesshouldbeput.Cellphones/cameraphonesaretobeturnedoffandnotusedduringanytestingorexaminationperiod. During the testing session cell phones/camera phones are to be stored in a knapsack or purse, and may not be placed on the table, desktop, or individuals lap. Students may access the internet during class sessions for only instructor authorized, classrelated purposes. Page3of5 COMMUNICATION MSCI331IntroductiontoOptimization A.Obeidi I will make every effort to respond to your emails within a reasonable time. When you email me please include your full name and UW ID number. Do not email me asking to explain or clarify concepts; lecture time, tutorials, and office hours are set for that purpose. REEVALUATIONOFCOURSEWORK If you feel you deserve more marks on any given quiz or assignment, first carefully check the provided solutions and determine where and how many marks you believe you deserve. Contact the TA to arrange a convenient time to discuss the work and your complaint.IfyoufeelthattheTAdidnotrespondadequatelytoyourgrievancecontactme. On the other hand, to discuss your midterm exam mark, look carefully through your midterm answer paper when it is returned to you. If you are absolutely certain that you deserve more marks on your midterm exam, you must give it back immediately. A photocopy of it will be made and the original will be returned to you. You then have at most two weeks to make specific demands aboutyourmidtermexam. ACADEMICINTEGRITY,GRIEVANCE,DISCIPLINE,APPEALSANDNOTEFORSTUDENTSWITHDISABILITIES Thefollowingstatementsweretakenfromwww.uwaterloo.ca/accountability/documents/courseoutlinestmts.pdf: AcademicIntegrity:Inordertomaintainacultureofacademicintegrity,membersoftheUniversityofWaterloocommunity are expected to promote honesty, trust, fairness, respect and responsibility. [Check www.uwaterloo.ca/academicintegrity/ formoreinformation.] Grievance: A student who believes that a decision affecting some aspect of his/her university life has been unfair or unreasonable may have grounds for initiating a grievance. Read Policy 70, Student Petitions and Grievances, Section 4, www.adm.uwaterloo.ca/infosec/Policies/policy70.htm. When in doubt please be certain to contact the departments administrativeassistantwhowillprovidefurtherassistance. Discipline: A student is expected to know what constitutes academic integrity [check www.uwaterloo.ca/academicintegrity/] to avoid committing an academic offence, and to take responsibility for his/her actions. A student who is unsure whether an action constitutes an offence, or who needs help in learning how to avoid offences (e.g., plagiarism, cheating) or about rules for group work/collaboration should seek guidance from the course instructor, academic advisor, or the undergraduate Associate Dean. For information on categories of offences and types of penalties,studentsshouldrefertoPolicy71,StudentDiscipline,www.adm.uwaterloo.ca/infosec/Policies/policy71.htm.For typical penalties check Guidelines for the Assessment of Penalties, www.adm.uwaterloo.ca/infosec/guidelines/penaltyguidelines.htm. Appeals: A decision made or penalty imposed under Policy 70 (StudentPetitions and Grievances) (other than a petition) or Policy 71 (Student Discipline) may be appealed if there is a ground. A student who believes he/she has a ground for an appealshouldrefertoPolicy72(StudentAppeals)www.adm.uwaterloo.ca/infosec/Policies/policy72.htm. Note for Students with Disabilities: The Office for persons with Disabilities (OPD), located in Needles Hall, Room 1132, collaborates with all academic departments to arrange appropriate accommodations for students with disabilities without compromising the academicintegrityofthecurriculum.Ifyourequireacademicaccommodationstolessentheimpactofyourdisability,pleaseregister withtheOPDatthebeginningofeachacademicterm. Plagiarism detection software (Turnitin) will be used to screen assignments and project reports in this course. This is being done to verify that use of all materials and sources in assignments is documented. Students will be given an optioniftheydonotwanttohavetheirassignmentscreenedbyTurnitin.Inthefirstweekoftheterm,detailswillbe providedaboutarrangements andalternativesfortheuseofTurnitininthiscourse.Studentshavethe righttosayNO to submitting to Turnitin. If you do not wish to have your work submitted to Turnitin let me know in writing by January 13,2012. Page4of5 MSCI331IntroductiontoOptimization COURSESCHEDULEFORWINTER2012 A.Obeidi Noticethattopiccoveragemaybeadjustedasthecourseprogresses. Week Chapter Reading Topics Jan1719 Jan2426 Introductiontothecourse LinearProgramming(LP):Formulation LinearProgramming(LP):Formulationand graphicalsolution GeometricpropertiesandtheSimplexAlgorithm Sensitivityanalysis 4 5&6.8 Jan31Feb2 Duality 6 Feb79 Feb 15 Dualityandsensitivityanalysis Feb1416 Integerprogramming(IP) 9 Feb 28-March 1 Integerprogramming(IP) 9 March68 Decisionanalysis Class notes March1315 Networkmodels:Formulation (TransportationandTranshipment) 78 March2022 Class+Projectpresentations March2729 Class+Projectpresentations Jan35 Jan1012 3 3 Assigningteams (Jan13) Assignment andQuiz Quiz1(Jan.18) PartI:Literature reviewreport (Feb3) Quiz2(Feb2) Midterm Examination Finalexamtobeannounced Page5of5 Project Assignment1 (Feb13) PartII:Minicase finalreport (March16) Quiz3(March 14) Assignment2 (March26)
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:

Waterloo - ECE - 111
Lecture 1 (Part 2): IntroductionEn-hui YangUniversity of WaterlooEn-hui YangECE 415: Multimedia CommunicationsClassical Model of a Digital Communication SystemsLayered StructureBinary interfaceSourceSourceencoderEncrypterChannelencoderDistor
Waterloo - ECE - 111
Lecture 2: Digital ImagesEn-hui YangUniversity of WaterlooEn-hui YangECE 415: Multimedia CommunicationsDigital Representation of ImagesAn image consists of a set of units called pixels which areorganized in the form of a two-dimensional array. On a
Waterloo - ECE - 111
Lecture 3: Digital VideoEn-hui YangUniversity of WaterlooEn-hui YangECE 415: Multimedia CommunicationsRepresentation of Digital VideoDigital video is represented by a sequence of moving digitalimages shown in quick succession. Each moving image is
Waterloo - ECE - 111
Lecture 4: The Notion of Lossless CodesEn-hui YangUniversity of WaterlooEn-hui YangECE 415: Multimedia CommunicationsGeneral Lossless CodesNotationX : a source alphabet with its cardinality 2; in typical textcompression, X = cfw_0, 1, , 255.X n (
Waterloo - ECE - 111
Lecture 5: EntropyEn-hui YangUniversity of WaterlooEn-hui YangECE 415: Multimedia CommunicationsEntropyLet X be a random variable taking values in X with probabilitymass function (pmf) p(x ) = Prcfw_X = x , x X , whereX = cfw_a0 , a1 , , aJ 1 .De
Waterloo - ECE - 111
Lecture 6: Connecting Entropy to UniquelyDecodable CodesEn-hui YangUniversity of WaterlooEn-hui YangECE 415: Multimedia CommunicationsKraft InequalityC (X1 )C (X2 ) X = X1 X2 Memorylesscoder CDMSRate RKraft inequalityEntropy H (X )Figure: 6.
Waterloo - ECE - 111
Lecture 7: Huffman CodingEn-hui YangUniversity of WaterlooEn-hui YangECE 415: Multimedia CommunicationsGiven a pmf pj = p(aj ), 0 j J 1, overX = cfw_a0 , a1 , , aJ 1 we now look at how to design an optimal prex code C such thatJ 1R=pj |C (aj )|
Waterloo - ECE - 111
Lecture 8: Arithmetic CodingBasic IdeaEn-hui YangUniversity of WaterlooEn-hui YangECE 415: Multimedia CommunicationsDrawbacks of Huffman CodingIn principle, the Huffman coding algorithm can also be appliedto design optimal prex codes with block len
Waterloo - ECE - 111
Lecture 9: Adaptive Arithmetic CodingEn-hui YangUniversity of WaterlooEn-hui YangECE 415: Multimedia CommunicationsUniversal Source CodingIn Huffman coding and arithmetic coding discussed so far, boththe encoder and decoder are assumed to know the
Waterloo - ECE - 111
Lecture 10: Run Length CodingEn-hui YangUniversity of WaterlooEn-hui YangECE 415: Multimedia CommunicationsBasic IdeaRun length coding is efcient for data sequences where longsegments of repeated symbols (runs) appear. Consider, forexample, a bina
Waterloo - ECE - 111
Lecture 11: Lempel-Ziv CodingEn-hui YangUniversity of WaterlooEn-hui YangECE 415: Multimedia CommunicationsLempel-Ziv codes are universal codes which are based onstring matching. Since the original work of Ziv and Lempel inlater 1970s, many variant
Waterloo - ECE - 111
ECE 415: Multimedia CommunicationsHomework Set 11Due Monday, Jan. 23, 2012 (Hand in to your TA)The following problems are from Section 5 (Exercises) of Chapter 3 of Reference Book [1]:1. Problem 22. Problem 43. Problem 54. Problem 65. Problem 8
Waterloo - ECE - 111
ECE 415: Multimedia CommunicationsHomework Set 21Due Monday, Jan. 23, 2012 (Hand in to your TA)Problem 1 Determine which of the following codes is uniquely decodable.(a) cfw_0, 10, 11.(b) cfw_0, 01, 11.(c) cfw_0, 01, 10.(d) cfw_00, 01, 11, 001, 01
Waterloo - ECE - 111
ECE 415: Multimedia CommunicationsHomework Set 31Due Monday, Jan. 30, 2012 (Hand in to your TA)Problem 1 The probability mass function of X is given in (1):x1234p(x) 0.124 0.187 0.3 0.389(1)(a) Letnj = log p(X = j ) , 1 j 4.Design a prex cod
Waterloo - ECE - 111
Tutorial 9 ECE 415 Jin Meng HW 7 Problem 1 Consider a random vector U=(U1U2U3U4)^T with zero mean and covariance matrix "$$$$!=$$$$$#13 %872''131387''22'1313 '7822''131378'22&132 Comput
Waterloo - ECE - 111
ECE428 Winter 2012Part II: CryptographyAssignment 11. The following is the result of a Vigenere cipher of unknown period Explain how you would go aboutdeciphering the cipher? You may use any of the many tools available on the internet to help you actu
Waterloo - ECE - 111
Tutorial on Assignment 2ECE 428University of WaterlooWinter 2012Question 1 (6.1)Public-key cryptography can be used for encryptionand key exchange. Furthermore, it has someproperties (such as nonrepudiation) which are notoffered by secret key cryp
Waterloo - PSYCH - 101
Introduction to PsychologyPsychology 101 (Section 001)University of WaterlooWinter 2012COURSE SYLLABUSInstructor:Class Meeting:Office:Office Hours:Phone:E-mail:Course Website:Richard EnnisTuesday, 6:30 - 9:20 p.m., HH159PAS 3017Thursday, 10
Waterloo - PSYCH - 101
lecture1.txt2012-02-08*The Basic ModelEnvironment -| Person -> Behaviour -> Outcome-Person- Innate tendencies of person determining how a person behavesPerson interacts with environment (denoted as P x E)- Environment affects person,e.g. social
Waterloo - PSYCH - 101
lecture2.txt2012-02-12# Background: The Industrial RevolutionEarly employment for children in the case of civilians- Mines, Cleaning chimney, factory maintenance- no child labor laws# Emergence of different point of views on childrenSapling- Just
Waterloo - PSYCH - 101
lecture3.txt2012-02-12Jan 17, 2012Lecture 3* Review of Cog. Dev: Jean Piaget- Children are genuinely different from the way they think and behave in comparison toadults- public education- child labour laws- SchemaP x E (child interacting with wo
Waterloo - PSYCH - 101
lecture4.txt2012-02-12Lecture 4Sensation and PerceptionPXEPerson perceives the world.-Two Inseparable ProcessesSensation- Physical sensing of environment- Physiological processes- Relatively objective- Learning and experience not required- eg
Waterloo - PSYCH - 101
lecture6.txt2012-02-07Lecture 6Feb 7, 2012Midterm: 6:30 to 7:45Comprehension from study guide: have a look=Memory*Sensation and Perception Across Time- Memory: Capacity to store info that's been previously processed*3 phases of memory-Encoding
Waterloo - PSYCH - 101
Lecture'5'(January'31,'2012)'Lecture'Outline'(midterm'upto'end'of'this'lecture)''Associations'! Volkswagen+girl+=+aroused.next+time,+when+you+see+a+Volkswagen,+u+get+aroused?!+! Associating+something+with+something+positive,+may+actually+have+an+imp
Waterloo - PSYCH - 101
Page 1 of 71TipsChapter 3: The Nature and Nurture of Behavior - Exam 1Exam BuilderToolboxHelpTest BanksEdit Exam 1Title & Introductory Text : Add/Edit the assessment's title and introductory text by clicking the link below. The titleand introduct
UPR Humacao - ECON - 101
Captulo IIIFRDRIC CHOPIN (1810-49)III. 1. Obras para pianoBenedetto seala que la obra para piano de Chopin se puede clasificar de acuerdo a losmodos, tiempos y lugares de su actividad pianstica en diversos gneros y formas. (104)Salvo algunas cancione
Minnesota - CSCI - 5512
Course Overview, Probability BasicsCSci 5512: Artificial Intelligence II Instructor: Arindam BanerjeeJanuary 18, 2012Instructor: Arindam BanerjeeCourse Overview, Probability BasicsGeneral InformationCourse Number: CSci 5512 Class: Mon Wed 12:45-02:0
Minnesota - CSCI - 5512
Exact InferenceCSci 5512: Artificial Intelligence II Instructor: Arindam BanerjeeJanuary 21, 2012Instructor: Arindam BanerjeeExact InferenceOverview: Inference TasksSimple Queries: Compute posterior marginals P(b|j, m)Instructor: Arindam BanerjeeE
Minnesota - CSCI - 5512
The Sum-Product AlgorithmCSci 5512: Artificial Intelligence II Instructor: Arindam BanerjeeJanuary 30, 2012Instructor: Arindam BanerjeeThe Sum-Product AlgorithmFactor GraphsMany problems deal with global function of many variablesInstructor: Arinda
Minnesota - CSCI - 5512
Approximate Inference: StochasticCSci 5512: Artificial Intelligence II Instructor: Arindam BanerjeeFebruary 1, 2012Instructor: Arindam BanerjeeApproximate Inference: StochasticBayesian Networks with LoopsP(C) .50CloudyC P(S|C) T .10 F .50Sprinkle
Minnesota - CSCI - 5512
Approximate Inference: MCMCCSci 5512: Artificial Intelligence II Instructor: Arindam BanerjeeFebruary 6, 2012Instructor: Arindam BanerjeeApproximate Inference: MCMCProblemsPrimarily of two types: Integration and OptimizationInstructor: Arindam Bane
Minnesota - CSCI - 5512
Junction TreesCSci 5512: Artificial Intelligence II Instructor: Arindam BanerjeeFebruary 13, 2012Instructor: Arindam BanerjeeJunction TreesReparameterizationConsider a Bayesian network p(a, b, c, d) = p(a|b)p(b|c)p(c|d)p(d)Instructor: Arindam Baner
Minnesota - CSCI - 5512
Probabilistic Reasoning over Time: Part ICSci 5512: Artificial Intelligence II Instructor: Arindam BanerjeeFebruary 15, 2012Instructor: Arindam BanerjeeProbabilistic Reasoning over Time: Part IOutlineTime and uncertaintyInstructor: Arindam Banerjee
Minnesota - CSCI - 5512
Probabilistic Reasoning over Time: Part IICSci 5512: Artificial Intelligence II Instructor: Arindam BanerjeeFebruary 22, 2012Instructor: Arindam BanerjeeProbabilistic Reasoning over Time: Part IIHidden Markov ModelsXt is a single, discrete variable
Minnesota - CSCI - 5512
Making Simple DecisionsCSci 5512: Articial Intelligence IIInstructor: Arindam BanerjeeFebruary 27, 2012Instructor: Arindam BanerjeeMaking Simple DecisionsPreferencesApL1pBA lottery is a situation with uncertain prizesLottery L = [p , A; (1 p
Minnesota - CSCI - 5512
Markov Decision ProcessesCSci 5512: Articial Intelligence IIInstructor: Arindam BanerjeeFebruary 29, 2012Instructor: Arindam BanerjeeMarkov Decision ProcessesSequential Decision ProblemsSearchexplicit actionsand subgoalsPlanninguncertaintyand
Minnesota - CSCI - 5512
Game TheoryMechanism DesignCSci 5512: Articial Intelligence IIInstructor: Arindam BanerjeeMarch 7, 2012Instructor: Arindam BanerjeeGame TheoryMechanism DesignOutlinePayos and StrategiesDominant Strategy EquilibriumNash EquilibriumMaximin Strate
Minnesota - CSCI - 5512
Learning From ObservationsCSci 5512: Articial Intelligence IIInstructor: Arindam BanerjeeMarch 19, 2012Instructor: Arindam BanerjeeLearning From ObservationsOutlineLearning AgentsInductive LearningDecision Tree LearningMeasuring Learning Perform
Minnesota - CSCI - 5512
Learning TheoryCSci 5512: Articial Intelligence IIInstructor: Arindam BanerjeeMarch 21, 2012Instructor: Arindam BanerjeeLearning TheoryPAC LearningLearning from a Hypothesis Space HInstructor: Arindam BanerjeeLearning TheoryPAC LearningLearning
Minnesota - CSCI - 5512
Learning with Hidden VariablesCSci 5512: Articial Intelligence IIInstructor: Arindam BanerjeeApril 18, 2012Instructor: Arindam BanerjeeLearning with Hidden VariablesHidden VariablesReal world problem have hidden variablesInstructor: Arindam Banerj
Minnesota - CSCI - 5512
Neural NetworksCSci 5512: Articial Intelligence IIInstructor: Arindam BanerjeeMarch 28, 2012Instructor: Arindam BanerjeeNeural NetworksBrain1011 neurons of > 20 types, 1014 synapses, 1ms10ms cycle timeSignals are noisy spike trains of electrical p
Minnesota - CSCI - 5512
Linear ModelsCSci 5512: Articial Intelligence IIInstructor: Arindam BanerjeeApril 2, 2012Instructor: Arindam BanerjeeLinear ModelsUnivariate Linear Regression(a)(b)hw (x ) = w1 x + w0nnL2 (yi , hw (xi ) =2Loss (hw ) =i =1Instructor: Arinda
Minnesota - CSCI - 5512
Convex FunctionsA function f is convex if dom(f ) is a convex set and [0, 1]f (x1 + (1 )x2 ) f (x1 ) + (1 )f (x2 )A function f is concave if f is convexInstructor: Arindam BanerjeeConvex Analysis and OptimizationFirst Order Conditionsf is convex i
Minnesota - CSCI - 5512
Nonparametric ModelsCSci 5512: Articial Intelligence IIInstructor: Arindam BanerjeeApril 4, 2012Instructor: Arindam BanerjeeNonparametric ModelsParametric Vs NonparametricParametric modelsInstructor: Arindam BanerjeeNonparametric ModelsParametri
Minnesota - CSCI - 5512
Support Vector MachinesCSci 5512: Articial Intelligence IIInstructor: Arindam BanerjeeApril 8, 2012Instructor: Arindam BanerjeeSupport Vector MachinesLinear SeparatorsInstructor: Arindam BanerjeeSupport Vector MachinesLinear SVMs: Separable Case
Minnesota - CSCI - 5512
BoostingCSci 5512: Articial Intelligence IIInstructor: Arindam BanerjeeApril 11, 2012Instructor: Arindam BanerjeeBoostingEnsemble LearningUse a collection of hypothesis from the hypothesis spaceInstructor: Arindam BanerjeeBoostingEnsemble Learni
Minnesota - CSCI - 5512
Statistical LearningCSci 5512: Articial Intelligence IIInstructor: Arindam BanerjeeApril 16, 2012Instructor: Arindam BanerjeeStatistical LearningFull Bayesian learningThe Bayesian view of learningInstructor: Arindam BanerjeeStatistical LearningF
Minnesota - CSCI - 5525
PCA vs FA! PCA! FAProject x to zCombine z to xz = WT(x !)x ! = Vz + !xzzxE. Alpaydin, Introduction to Machine LearningFactor Analysis! Finda small number of factors z, which whencombined generate x :xi !i = vi1z1 + vi2z2 + . + vikzk + !iw
Minnesota - CSCI - 5525
CSCI 5525: Machine Learning (Spring 2012)Dimension ReductionRui KuangDepartment of Computer Science and EngineeringUniversity of MinnesotaFeature SelectionNP-hard to search through all the combinations Needheuristic solutionsThe assumption is bas
Minnesota - CSCI - 5525
CSCI 5525: Machine Learning (Spring 2012)Dimension ReductionRui KuangDepartment of Computer Science and EngineeringUniversity of MinnesotaLinear Discriminant Analysis Finda low-dimensionalspace such that when xis projected, classes arewell-separ
Minnesota - CSCI - 5525
CSCI 5525: Machine Learning (Spring 2012)Course OverviewRui KuangDepartment of Computer Science and EngineeringUniversity of MinnesotaWelcome to CSci 5525Course: Machine LearningInstructor: Rui Kuang (Ray), Assistant Professor (CS&E) Contact:Offi
Minnesota - CSCI - 5525
CHAPTER 5:Multivariate MethodsE. Alpaydin, Introduction to Machine LearningMultivariate Data Multiplemeasurements (sensors) d inputs/features/attributes: d-variate N instances/observations/examples111X X X 12d 22212dX X X X= NN
Minnesota - CSCI - 5525
CSCI 5525: Machine Learning (Spring 2012)Bayes DecisionTheory andParametric ModelsRui KuangDepartment of Computer Science and EngineeringUniversity of MinnesotaRegression exampleCoefficients increase inmagnitude as orderincreases:1: [-0.0769, 0
Minnesota - CSCI - 5525
CSCI 5525: Machine Learning (Spring 2012)Bayes DecisionTheory andParametric ModelsRui KuangDepartment of Computer Science and EngineeringUniversity of MinnesotaParametric vs NonparametricParametric methods: Amodel (usually a type of simple distr
Minnesota - CSCI - 5525
CSCI 5525: Machine Learning (Spring 2012)Bayes DecisionTheory andParametric ModelsRui KuangDepartment of Computer Science and EngineeringUniversity of MinnesotaParametric Classification Discriminantfunctiongi ( x ) = p( x | Ci ) P (Ci )orgi (
Minnesota - CSCI - 5525
CSCI 5525: Machine Learning (Spring 2012)Supervised LearningRui KuangDepartment of Computer Science and EngineeringUniversity of MinnesotaSupervised Learning Classification RegressionInput Feature Space" x1 %$'$ x2 'x = $ . '$'$ . '$xD '#&
Minnesota - CSCI - 5525
CSCI 5525: Machine Learning (Spring 2012)Supervised LearningRui KuangDepartment of Computer Science and EngineeringUniversity of MinnesotaSupervised Learning ClassificationData: RegressionX = cfw_x t,r t N=1tX = cfw_x t,r t N=1trt " #(Clas
Minnesota - CSCI - 5525
CSCI 5525: Machine Learning (Spring 2012)Supervised LearningRui KuangDepartment of Computer Science and EngineeringUniversity of MinnesotaNoise and Model ComplexityGiven similar training error,use the simpler oneSimpler to use (lowercomputational
Minnesota - CSCI - 5525
CSCI 5525: Machine Learning (Spring 2012)NonparametricMethodsRui KuangDepartment of Computer Science and EngineeringUniversity of MinnesotaDensity EstimationGiven the training set X=cfw_xtt drawn iid from p(x)Divide data into bins of size h Histo
Minnesota - CSCI - 5525
CSCI5525: Machine Learning (Spring 2012)ClusteringRui KuangDepartment of Computer Science and EngineeringUniversity of MinnesotaExpectation-Maximization (EM)Complete likelihood, Lc( |X,Z), in terms of x and zLc (" | X ) = log # p(x t , zt | ") = $t
Minnesota - CSCI - 5525
CSCI 5525: Machine Learning (Spring 2012)Linear DiscriminationRui KuangDepartment of Computer Science and EngineeringUniversity of MinnesotaLikelihood- vs. Discriminantbased ClassificationLikelihood-based: Assume a model for p(x|Ci),use Bayes rule