[pic 1]COURSE OUTLINEGIFT SCHOOL OF COMPUTING SCIENCECS-415Artificial IntelligenceB.S Computer ScienceFall Semester, 2018Faculty: GIFT School of Computing ScienceCredit hours: 3 Course level:UndergraduateCampus/Location/Instruction Mode:GIFT University/On Campus/In PersonCourse Convenor:Qamar AskariConsultation hours:TBDPre-requisite:Analysis of AlgorithmTimingPlease see the timetableThis document was last updated:27th Oct, 2018BRIEF COURSE DESCRIPTIONThe goal of this course is to introduce the students to the field of AI and various search problems and problem solving methods used in AI. In this course they will solve various problems using AI algorithms. At the end of the course it is expected that the students will appreciate the importance of AI in different domains and be able to understand, design and to an extent implement some basic AI systems.COURSE AIMS AND LEARNING OUTCOMESAfter completing this course student should be able to:Understand and build intelligent agents.Solve search problems by picking up an appropriate algorithm or by designing their own solution.Solve optimization problems using approximate algorithms.Make their programs able to learn and improve with time.Represent knowledge in appropriate form and reason on that knowledge using a specific language.Learn about some successful applications of artificial intelligence.TopicRecourse WeekIntroduction to AIDefinitionIntelligent Agents and environmentsApplication areas and applicationsChap 1,2 & handout/internet referenceWeek – 1Problem solving, Games & SearchingProblem formulation & example problemsUninformed and informed searchesBFS, DFS, DLS, ID and bidirectional searchBFS, Branch and bound, A* searchLocal & global searchHill climbing, beam search, simulated annealingAdversarial searchMinimax search, α-β pruning, games with element of chance, 3 player gameChap 3,4 & 5Week 2-5Optimization & MetaheuristicsIntroduction and example problemsExact algorithmsApproximate algorithms/MetaheuristicsGenetic AlgorithmIntroduction to PSO/ACO etc.Extra resourceWeek 6-7MidtermWeek 8 LearningSupervised learningRegression/Neural networksUnsupervised learningK-means algorithmIntroduction to reinforcement learningExtra Resource Week 8-10Knowledge representation, logic & reasoningTypes of knowledgeKnowledge representationsPropositional logicFirst order logicReasoningExpert SystemsPrologUncertain reasoningProbability basicsNaïve Bayes rule and its useStatistical learning using Naïve BayesChap 7,8, 9 and 13Week 10-13Application areasComputer visionSegmentation & recognitionInformation retrievalClustering and classificationGamingMove planning etc.RoboticsMotion & path planningA brief introduction to above areas and application of AI in above areas will be talked about.Extra Resource Week 14Project evaluationWeek 15-16TEXTBOOKArtificial Intelligence - A Modern Approach by Stuart Russel and Peter Norvig, Prentice Hall, Third Edition, 2010.PROJECTThis is a very important part of this course which can be observed from weightage assigned to project. In very first week students will be given an assignment which will help them to decide a project by the end of 3rd week. In addition to giving you a way to learn how AI techniques are applied at larger scale the project could also be extendible to FYP in next semester.