{"id":7822,"date":"2019-12-15T03:28:22","date_gmt":"2019-12-15T03:28:22","guid":{"rendered":"https:\/\/www.inspirenignite.com\/vtu\/artificial-intelligence-as-8th-sem-syllabus-for-vtu-be-2017-scheme-professional-elective-5\/"},"modified":"2019-12-15T03:28:22","modified_gmt":"2019-12-15T03:28:22","slug":"artificial-intelligence-as-8th-sem-syllabus-for-vtu-be-2017-scheme-professional-elective-5","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/vtu\/artificial-intelligence-as-8th-sem-syllabus-for-vtu-be-2017-scheme-professional-elective-5\/","title":{"rendered":"Artificial Intelligence AS 8th Sem Syllabus for VTU BE 2017 Scheme (Professional Elective-5)"},"content":{"rendered":"

Artificial Intelligence detail syllabus for Aerospace Engineering (As), 2017 scheme is taken from VTU<\/a> official website and presented for VTU students. The course code (17AS831), and for exam duration, Teaching Hr\/week, Practical Hr\/week, Total Marks, internal marks, theory marks, duration and credits do visit complete sem subjects post given below.<\/p>\n

For all other as 8th sem syllabus for be 2017 scheme vtu you can visit AS 8th Sem syllabus for BE 2017 Scheme VTU Subjects<\/a>. For all other Professional Elective-5 subjects do refer to Professional Elective-5<\/a>. The detail syllabus for artificial intelligence is as follows.<\/p>\n

Course Objectives:<\/h4>\n

This course will enable students to<\/p>\n

    \n
  1. Understand the basic techniques of artificial intelligence.<\/li>\n
  2. Understand the Non-monotonic reasoning and statistical reasoning.<\/li>\n
  3. Acquire the knowledge on filler structures, and understanding.<\/li>\n<\/ol>\n

    Module 1 <\/h4>\n

    For complete syllabus and results, class timetable and more pls download iStudy<\/a>. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.<\/p>\n

    Module 2 <\/h4>\n

    Knowledge Representation Issues : Representations And Mappings, Approaches To Knowledge Representation. Using Predicate Logic : Representation Simple Facts In Logic, Representing Instance And Isa Relationships, Computable Functions And Predicates, Resolution Representing Knowledge Using Rules : Procedural Versus Declarative Knowledge, Logic Programming, Forward Versus Backward Reasoning.\n<\/p>\n

    Module 3 <\/h4>\n

    Symbolic Reasoning Under Uncertainty : Introduction To Nonmonotonic Reasoning, Logics For Non-monotonic Reasoning. Turning performance limitations. Drag estimation. Take-off and landing – methods, procedures and data reduction. Statistical Reasoning : Probability And Bays’ Theorem, Certainty Factors And Rule-Base Systems, Bayesian Networks, DempsterShafer
    \nTheory, Fuzzy Logic.\n<\/p>\n

    Module 4 <\/h4>\n

    For complete syllabus and results, class timetable and more pls download iStudy<\/a>. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.<\/p>\n

    Module 5 <\/h4>\n

    Understanding: What is understanding? , What makes it hard?, As constraint satisfaction. Natural Language Processing: Introduction, Syntactic Processing, Semantic Analysis, Semantic Analysis, Discourse And Pragmatic Processing, Spell Checking. Connectionist Models : Introduction: Hopfield Network, Learning In Neural Network, Application Of Neural Networks, Recurrent Networks, Distributed Representations, Connectionist AI And Symbolic AI\n<\/p>\n

    Course Outcomes:<\/h4>\n

    After studying this course, students will be able to:<\/p>\n

      \n
    1. Apply the basic techniques of artificial intelligence.<\/li>\n
    2. Distinguish Non-monotonic reasoning and statistical reasoning.<\/li>\n
    3. Evaluate the natural language processing and connectionist models.<\/li>\n<\/ol>\n

      Graduate Attributes (as per NBA):<\/h4>\n