ࡱ> c #bjbj .^\^\y::j,2:K2M2M2M2M2M2M2$4H7q2-q22K2K2: !,!gsF7! 72202A!R77!!7##q2q2i27:> x: Artificial Intelligence CSE 5290, Fall 2017 Instructor: Debasis Mitra, Ph.D. Office: Harris 325 E-mail:  HYPERLINK "mailto:dmitra@zach. fit.edu" dmitra at cs.fit. edu Class Home Page:  HYPERLINK "http://www.cs.fit.edu/~dmitra/ArtInt/" http://www.cs.fit.edu/~dmitra/ArtInt/ Class6:30 pm - 7:45 pmTRFrederick C. Crawford Bldg 220Office Hours: MT 12:30-2:30 pm (or by appointment) Tentative Grading plan: Quizzes/Class Exams: 40%, Project: 30%, Final exam: 30% Detail plan for Fall 2017: d" 29 days Search 4, Constraints 3, Auto Reasoning 4, Prob Reasoning 4, Mach Learning 5, Ethics 1, Exams 3, Std presentations 4 DateActivities planned/ performed Aug 22, TIntroduction to AI, and background Aug 24, R (Jan 16, M  MLK day)SEARCH: 8-puzzle: https://n-puzzle-solver.appspot.com/ BFS- Uniform Cost; DFS-Depth Limited, Iterative-deepening Iterative-deepening, From my slidesIntro: Slide 18-end Search: Djikstra, BFS, DFS, DLS, IDSAug 29, TSearch: Heuristic Search, A*, MySl 16-26 From my slidesAug 31, R MySl 27-33: IDA*, SMA* Text Slides 4a Sep 5, T Local search from Text-slides: Local search, Hill-climbing, Gradient search, Local beam search, Online search Simulated Annealing, Genetic Algorithm, Random walk My Slides: 44-end: GO OVER YOURSELF SEARCH: Adversarial Min-Max, Alpha-beta pruning, Mover ordering, Evaluation function, Forward search Sep 7, ROnly one student attended: discussed project with himSep 12, TNo class for Hurricane IrmaSep 14, RSearch problems. Project ideas. REASONING WITH CONSTRAINTS: Motivating with Map/Graph coloring, Backtracking, Forward CheckingSep 19, TREASONING WITH CONSTRAINTS: Node-Arc-Path-Global consistency Project groups? Sep 21, RConstraint reasoning continued SAT problem from Algorithms-Complexity slides Sep 26, TSPATIO-TEMPORAL CONSTRAINT REASONING from my slides (All slides are within syllabus). A relevant web page: https://www.ics.uci.edu/~alspaugh/cls/shr/allen.html Projects discussed Sep 28, RAUTOMATED REASONING: Syntax-Semantics-Model, Satisfaction-Entailment-Inference procedure-Validity; Exam -1 on Search and Constraint Reasoning (up to Ch 6, with materials covered only in class)[4 questions, 40 points Undergrad, 50 points Grad, 45 min for Undergrad part, Grad questions are take home]Oct 3, TComplexity theory AUTOMATED REASONING - continued: Propositional Knowledge Base, Model checking algorithm, Forward chaining algo, Backward chaining algo (up to sl#66)Oct 5, RCNF, Resolution Algo (p255), Horn Clause, Definite clause Project discussion UG project plan deadlineOct 10, T (Fall break)- - - Oct 12, RAUTOMATED REASONING: First Order Logic-Motivation; Model-Interpretation-Quantifiers-Inferencing; Exc in Ch8  Grad Proj first deliverableOct 17, TForward/Backward Chaining, Resolution-DB search, Completeness-Herbrand Universe, Resolution strategies Oct 19, RUnification, Forward Chaining, Backward Chaining, Grades are on CanvasOct 24, TTest for students ONLY with the following grades: Ug < 25 and Grad <32: On search We will have some Saturday 2:30pm make up classesOct 26, RDr. Bhattacharyya in class:?Guest lecture on Formal verification  Second project deadline: hard copies for UG, submit to Dr. Bhattacharyya in class Grad submission: e-mail meOct 31, TResolution, Prolog language, ?CLIPS code Rete network? Exercises in Ch9.6, 9.9b, 9.24a-c MODELING UNCERTAINTY, Ch 14: Motivation Probability-Joint-Inference-Conditional-Bayes rule; Reasoning with probability, Node-structuring, Conditional IndependenceNov 2, RREASONING WITH UNCERTAINTY: Ch 14: Bayesian Net Nov 3 Friday 12 pm: my talk in CS Seminar, on Topological Data Analysis & ReasoningNov 7, T Bayes net continued Sample questions Ch13: 13.8, 14, 15 Grad Project Presentations: 12 min each groups status report, update your last written report as phase II submission before class: e-mail me Grad submissions on last Sunday will have grades reduced by 5 points as late allowed submission deadline, (Project Report-2)Nov 9, RGrad presentation continued with two groups send me slides not cloud pointers! Ch 18: MACHINE LEARNING: Decision tree, basics: up to slides 27 today Relational Probabilistic model, Dempster-Schaeffer Possibilistic reasoning, Fuzzy Logic Ch 15 Time in Bayes net, Markov chain, HMM, Kalman, Dynamic Bayes Net;Comments on presentations added to the project ideas document both for Grad and UG. Two absent UG Project Report-2: Yaqueen and Jiyai Nov 11, Sat, 2:30-3:45pm Make up class, Room: 11/11/2017 Sat 2:30 PM-4:00 PM 420CRF 230, Crawford Building Ch 18.6: MACHINE LEARNING continued: MDL, PAC, 18.6.1-2. Regression: Linear, Multi-variate, Non-linearNov 14, T18.6.3-4. Classifiers, Logistic regression, Perceptron 18.7.1-?. Basics of Artificial-neural Networks Nov 16, RArtificial neural network Exam-2 on Automated Reasoning and Probabilistic Reasoning Nov 21, TMACHINE LEARNING Ch 18.8: non-parametric, Ch 18.9-10: SVM, Ada-boost Project Final report due on November 28, T, i in class both UG and Grad, UG: hard copy (~5 pages), Grad: TBD (ignore any other date if you see at other places)Nov 23, R (Thanksgiving Day)- - -Nov 28, T MACHINE LEARNING: Evaluation Ch18.4 Problems from 14.14, 13.8Syllabus for Exam-3: Ch 7.5 CNF, Resolution, Fig 7.12 Ch 13 all Ch 14-14.4.1, 14.4.4 clustering Ch 18-18.3.3 [exclude 18.3.4 attribute choice],,18.3.5, for now], 18.3.5, 18.4-18.4.1, 18.4.3, 18.6-18.7.4 (exclude derivation on p735), 18.8-18.8.4 [[18.9 SVM, and K-means clustering will be included in the Final exam]]Nov 30, RClustering basics: K-means, Distance measure, Hierarchical clustering, Fuzzy C-means, from  HYPERLINK "https://en.wikipedia.org/wiki/K-means_clustering" https://en.wikipedia.org/wiki/K-means_clustering 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