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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