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Course: MAS 3300, Summer 2011
School: University of Florida
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&amp; Numbers Polys MAS3300 Prof. JLF King 4Jun2008 C1: 80pts C4: 80pts C5: 80pts Total: Notes exam. Please write solutions for C3,C4,C5 on separate sheets of paper. Write expressions unambiguously e.g, 1/a + b should be bracketed either [1/a] + b or 1/[a + b]. Be careful with negative signs! 40pts C3: Note. This is an open brain, open (pristine) Sigmon- 70pts C2: Exam-C 350pts Every if must...

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& Numbers Polys MAS3300 Prof. JLF King 4Jun2008 C1: 80pts C4: 80pts C5: 80pts Total: Notes exam. Please write solutions for C3,C4,C5 on separate sheets of paper. Write expressions unambiguously e.g, 1/a + b should be bracketed either [1/a] + b or 1/[a + b]. Be careful with negative signs! 40pts C3: Note. This is an open brain, open (pristine) Sigmon- 70pts C2: Exam-C 350pts Every if must be matched by a then. Short answer; show NO work. a C1: Let := Gcd(165, 63) and nd particular integers so that 165A + 63B = . Then = , A= , B = . Print name Ord: ............................................. b Note that Gcd(15, 21, 35) = 1. Find particular integers S, T, U so that 15S + 21T + 35U = 1: S= , T = , U = . [Hint: Gcd Gcd(15, 21), 35 = 1.] C2: Let AT AF Nei mean Always True, Always False, and Neither, respectively. Let represent q a general non-zero rational number and let x, y denote general irrationals. Please circle the best answer. The product x y is irrational. AT AF Nei The sum x + y is irrational. AT AF Nei q x is irrational. AT AF Nei q + x is irrational. AT AF Nei Essay questions. Please write in complete sentences. C3: Prove that is 15 irrational. Now prove that 3 + 5 is irrational. C4: Consider two posints A and B . Prove directly, using WO, that there is a posint D st. DZ = AZ + B Z . Honor Code: I have neither requested nor received [Hint: Dont forget to rst show that AZ + B Z has a non-zero element.] C5: Prove that there are innitely many prime numbers. End of Exam-C help on this exam other than from my professor (or his colleague). Signature: .........................................
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University of Florida - MAS - 3300
Numbers &amp; PolysMAS3300g ( 2 )=3Prof. JLF King4Jun2008Home-DNote. Please be sure to write expressions unambiguously e.g, the expression 1/a + b should be bracketedeither [1/a] + b or 1/[a + b]. Be careful with negativesigns!? Now use a full sheet
University of Florida - MAS - 3300
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University of Florida - MAS - 3300
Numbers &amp; PolysMAS3300 3244Exam-SProf. JLF King7Nov2005Total:275ptsOrdinal:Note. This is an open brain, open (pristine) SigmonNotes exam. Please write each solution on aPlease be sure to write exseparate sheet of paper.pressions unambiguously e.
University of Florida - MAS - 3300
Set theory MiscellanyJonathan L.F. KingUniversity of Florida, Gainesville FL 32611-2082, USAsquash@math.ufl.eduWebpage http:/www.math.u.edu/squash/1 May, 2006 (at 02:10 )For m a natnum, the value P (m) is a natnum-indexpair. Write this pair as (mN
University of Florida - MAS - 3300
Numbers &amp; PolysMAS3300Prof. JLF King8Mar2006Note. This is an open brain, open (pristine) SigmonNotes exam. Please write each solution on a separate sheetof paper.Please be sure to write expressions unambiguously e.g, the expression 1/a + b should be
University of Florida - MAS - 3300
Numbers &amp; PolysMAS3300Prof. JLF King3Apr2006Exam-UNote. This is an open brain, open (pristine) SigmonNotesexam, calculator permitted. Please write each of the two essays on separate sheets of paper, using complete grammaticalEnglish sentences. Use
University of Florida - MAS - 3300
Numbers &amp; PolysMAS3300V3: Consider the Fibonacci numbers (fn ) den=ned by f0 := 0, f1 := 1, and n Z : fn+1 =fn + fn1 . Prove by induction thatProf. JLF King20Nov2006Home-VNote. Permitted: Brain, SigmonNotes, calculator, com-:puter, webpage; but
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Deep BlueWhat is &quot;Deep Blue&quot;?Chess playing machine built by IBM in the 1990's. 2 versions. Deep Blue 1 lost to world chess champion Gary Kasparov in 1996. Deep Blue 2 defeated world chess champion, Gary Kasparov on May 11, 1997.Leading up to Deep Blue
SUNY Stony Brook - CSE - 352
Cse 352, Fall 2008Bradford Wagner and Brandon Haviland1. What is Fuzzy Logic2. The origin of Fuzzy Logic and its inventor3. Why use Fuzzy Logic4. How Fuzzy Logic works5. The applications of Fuzzy Logic in the realworld6. Where Fuzzy Logic is going
SUNY Stony Brook - CSE - 352
CSE 352 Articial IntelligenceHOMEWORK 1 (25pts)SOLVE Problems that add to 25pts. If you solve more, it willcount as 5pts EXTRA CREDIT.Problem 1 (5pts)Write detailed solution to problems 2, 4 from our BOOK, page 8.Problem 2 (5pts)Write detailed solu
SUNY Stony Brook - CSE - 352
Joseph Scarabino9/20/11HW 1Problem 12.One expert system that would be useful would be for a gas powered grill. Many problems can arise in a grill,especially a large one with several burners. This knowledge may not be known by everyone, but will freq
SUNY Stony Brook - CSE - 352
1ARTIFICIAL INTELLICENCE CSE352 HOMEWORK 1 SOLUTIONSBook page8, Problem 1Here is what one of the previous students wrote.I think computer troubleshooting and searching for ore deposits are two experttasks that might be suitable for an expert system.
SUNY Stony Brook - CSE - 352
CSE352HOMEWORK 2 (Decision Tree Learning 1) 15ptsTRAINING DATA SET FOR THE HOMEWORK: Class Attribute: Buys ComputerAgeIncomeStudent&lt;=30&lt;=303140&gt;40&gt;40&gt;403140&lt;=30&lt;=30&gt;40&lt;=3031403140&gt;40highhighhighmediumLowlowlowmediumlowmediumm
SUNY Stony Brook - CSE - 352
CSE352 Articial IntelligenceHomework 2, Part 2 - 15ptsProblem 1 1. Build a Decision Tree following ID3 (without Information Gain) Algorithm for the dataset of the 14 records from Lecture L8 slides. The Decision Tree must have a dierent ROOT thanthe on
SUNY Stony Brook - CSE - 352
Joseph Scarabino10/13/11HW 2 : pt 1Problem 11)2)Evaluate Predictive Accuracy:Tree 1:3 well classified / 6 total=50%Tree 2:2 well classified / 6 total=33%Problem 2Tree 1:Obj123456Age&gt;40&lt;=3031-40&lt;=30&gt;40&lt;=30IncomeHighLowLow
SUNY Stony Brook - CSE - 352
Nicholas TrombettaCSE352: ArtificialIntelligenceHW 2 SOLUTIONS_PROBLEM 1_Tree 1:root attribute = Credit_ratingFigure 1- Training Data Split by Credit_Rating_Figure 2 - Majority Voting applied to Credit=FairFigure 3 - Right subtree split by Age a
SUNY Stony Brook - CSE - 352
David Hayman HW2: Part 1 decision tree and explanation on last two pagesTraining data:Age&lt;=30&lt;=303140&gt;40&gt;40&gt;403140&lt;=30&lt;=30&gt;40&lt;=3031403140&gt;40IncomeHighHighHighMediumLowLowLowMediumLowMediumMediumMediumHighMediumStudentNoNo
SUNY Stony Brook - CSE - 352
1Cse352 AIHomework 3 (30pts )PART ONE: Classification Data and Rules (10 pts)Definition 1Given a classification dataset DB with a set A = cfw_a1, a2, an of attributes and a classattribute C with values cfw_c1, c2, ck (k classes),any expression;a1
SUNY Stony Brook - CSE - 352
CSE 352 Artificial Intelligence Hw4 - 25pts1. Find all possible resolvents ofA) = cfw_ cfw_a, b, cfw_a, b, c, cfw_a, c, cfw_c, b B) = cfw_ cfw_a, a, b, cfw_a, b, c, cfw_a, b, c, cfw_b 2. Use a proper Resolution strategies do decide whether is unsatisf
SUNY Stony Brook - CSE - 352
CSE352PROJECT HOMEWORK (10 extra points)TOOL WEKA Machine Learning Project:/ http:/www.cs.waikato.ac.nz/ ml/ You can use any other tool you nd on the web. Contact TA for help with the tool DATA PREPARATION THIS IS THE DATA YOU WILL USE FOR YOUR PROJECT.
SUNY Stony Brook - CSE - 352
INTRODUCTIONWhat is Artificial Intelligence?(chapter 1)Cse352Lecture Notes (1)Professor Anita WasilewskaIntroduction AI is a broad field. It means different thingsto different people. AI is concerned with getting computers todo tasks that requir
SUNY Stony Brook - CSE - 352
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Predicate Logic(part 1)(Chapter 2)CSE 352 Artificial IntelligenceProfessor Anita WasilewskaLecture Notes (3)Predicate Logic LanguageSymbols:1. P, Q, R predicates symbols, denote relations in reallife, countably infinite set2. x,y,z. variables, c
SUNY Stony Brook - CSE - 352
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SUNY Stony Brook - CSE - 352
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Classification Lecture Notes (1)(cse352)PART ONE: Supervised learning (Classification)Data format: training and test dataConcept, or class definitions and descriptionRules learned: characteristic and discriminantSupervised learning = classification
SUNY Stony Brook - CSE - 352
Introduction to SupervisedandUnsuspervisedLearningCse352 Lecture NotesProfessor Anita WasilewskaStony Brook University1Learning Main ObjectivesIndentification of data as a source of usefulinformation, called also a knowledgeUse of learned inform
SUNY Stony Brook - CSE - 352
Classification Lecture Notes (2)(cse352)Review, Training, Testing, PredictiveAccuracyProfessor Anita WasilewskaClassification Data Data format: a data table with key attribute removed.Special attribute- class attribute must be distinguishedage&lt;=3
SUNY Stony Brook - CSE - 352
Classification Lecture Notes (3)(cse352)DECISION TREE CLASSIFICATION(Supervised Learning)Professor Anita WasilewskaClassification LearningALGORITHMSDifferent ClassifiersDESCRIPTIVE:Decision Trees (ID3, C4.5)Rough SetsGenetic Algorithms STATIST
SUNY Stony Brook - CSE - 352
Building the Trees withSpecial (General) Majority Voting:Take Majority class at any chosen node of thetreeExamplesProfessor Anita WasilewskaTraining DatasetrecAgeIncomeStudentCredit_ratingBuys_computer (CLASS)r1&lt;=30HighNoFairNor2&lt;=30H
SUNY Stony Brook - CSE - 352
BASIC DECISION TREE INDUCTIONALGORITMLecture Notes on Learning (5)cse352Professor Anita WasilewskaStony Brook UniversityDecision Tree AlgorithmsShort History Late 1970s - ID3 (Interative Dichotomiser) by J. RossQuinlan. This work expanded on ear
SUNY Stony Brook - CSE - 352
Testing classifier accuracy(cse352)Professor Anita WasilewskaLecture Notes on Learning (6)Overview Introduction Basic Concept on Training and Testing Resubstitution (N ; N) Holdout (2N/3 ; N/3) x-fold cross-validation (N-N/x ; N/x) Leave-one-out
SUNY Stony Brook - CSE - 352
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LECTURE NOTEScse352Professor Anita WasilewskaNEURAL NETWORKSBackpropagation AlgorithmNeural Networks ClassificationIntroduction INPUT: classification data, i.e. data thatcontains a classification (class)attribute. WE also say that the class labe
SUNY Stony Brook - CSE - 352
Propositional ResolutionPart 1Short ReviewProfessor Anita WasilewskaCSE 352 Artificial IntelligenceSYNTAX dictionaryLiteral any propositional VARIABLE a ornegation of a variable a, a VAR,Example - variables: a, b, c, negation ofvariables: a, b, -
SUNY Stony Brook - CSE - 352
Propositional ResolutionPart 2Short ReviewProfessor Anita WasilewskaCSE 352 Artificial IntelligenceGOAL: Use Resolution to prove/ disapprove |= APROCEDUREStep 1: Write A and transform A info set ofclauses CLcfw_A using Transformation rules.Step 2
SUNY Stony Brook - CSE - 352
Propositional ResolutionPart 3Short ReviewProfessor Anita WasilewskaCSE 352 Artificial IntelligenceResolution Strategies We present here some Deletion Strategies anddiscuss their Completeness.Deletion Strategies are restriction techniques inwhich
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SUNY Stony Brook - CSE - 352
CSE532 Articial Intelligence PROJECT DESCRIPTIONBAKARY DATA - on the course web page. This is a classication data with TYPE DE ROCHE (Rock Type) as a CLASS attribute. There are 98 records with 48 attributes and 6 classes. Classes are: C1 : R. Carbonatees
SUNY Stony Brook - CSE - 352
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SUNY Stony Brook - CSE - 352
Waleed PervaizCSE352ComputerVisionisthetechnologythatenablesmachinestosee andobtaininformationfromdigitalimages.ItisseenasanintegralpartofAIinfieldssuchaspatternrecognitionandlearningtechniques.Stillinitsinfancy,withworkstartinginthelate1970s.Noexi
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Pete SwopeHome Work 1Date Due September 21, 2010Problem 1. Page 8 #2aOne task which may be suitable for an expert system is driving an automobile. Automating thedaily task of driving an automobile is a very useful task because human drivers become ti
SUNY Stony Brook - CSE - 352
Bryant ServelloCSE 352 Homework 19/21/10Problem 1(2)Expert System 1: Wildlife RecognitionDescription: Allow a user to identify an organism based on its traits and behaviorUse: This system would be useful for anyone who is curious about a plant or a
SUNY Stony Brook - CSE - 352
CSE 352 Articial Intelligence FALL 2011Professor Anita Wasilewskahttp:/www.cs.sunysb.edu/ cse352MeetsTuesday,Thursday 2:20 - 3:40pmPlace Humanities 3019Professor Anita Wasilewska e-mailanita@cs.sunysb.edu,Oce phone number: 632 8458Oce location:
SUNY Stony Brook - CSE - 352
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UGA - BIOL - 1104
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UGA - BIOL - 1104
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UGA - BIOL - 1104
Investigating the Affect of Salinity on Bacteria GrowthKayla MurphyLab Section: 12:20 ThursdayGLA: Kathy BowlesLab Partner: Hannah HallSpring 2011IntroductionHeterotrophic bacteria are the primary agents of decomposition for dead material. Theyact
UGA - BIOL - 1104
UGA - BIOL - 1104
Grading Form for Rough Draft of AQUATIC EXPERIMENT Lab ReportBIOL 1104LStudent name _Kayla Murphy_You have received the following score (out of 15 points) on your first version onthe Aquatic Experiment Lab Report._15_ /15This score is based primaril
UGA - BIOL - 1104
Investigating the Affect of Salinity on Bacteria GrowthKayla MurphyLab Section: 12:20 ThursdayGLA: Kathy BowlesLab Partner: Hannah HallSpring 2011IntroductionHeterotrophic bacteria are the primary agents of decomposition for dead material. Theyact
UGA - BIOL - 1104
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UGA - ENGL - 4320
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UGA - ENGL - 4320
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UGA - ENGL - 4320
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UGA - ENGL - 4320
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UGA - ENGL - 4320
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UGA - ENGL - 4320
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UGA - ENGL - 4640
Film as Literature 1-11-12- Can there ever be a perfect adaptation?Adaptation, remediation, narrativity-Novel as high culture, film as low-Seek fidelity between novel and film, which will never happen-Grammar in film is different edits and shots-