Artificial Intelligence detail syllabus for Aerospace Engineering (As), 2017 scheme is taken from VTU 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.
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. For all other Professional Elective-5 subjects do refer to Professional Elective-5. The detail syllabus for artificial intelligence is as follows.
Course Objectives:
This course will enable students to
- Understand the basic techniques of artificial intelligence.
- Understand the Non-monotonic reasoning and statistical reasoning.
- Acquire the knowledge on filler structures, and understanding.
Module 1
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Module 2
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.
Module 3
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
Theory, Fuzzy Logic.
Module 4
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Module 5
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
Course Outcomes:
After studying this course, students will be able to:
- Apply the basic techniques of artificial intelligence.
- Distinguish Non-monotonic reasoning and statistical reasoning.
- Evaluate the natural language processing and connectionist models.
Graduate Attributes (as per NBA):
- Engineering Knowledge.
- Problem Analysis.
- Design / development of solutions
- Interpretation of data
Question paper pattern:
- The question paper will have ten questions.
- Each full question consists of 16 marks.
- There will be 2 full questions (with a maximum of four sub questions) from each module.
- Each full question will have sub questions covering all the topics under a module.
- The students will have to answer 5 full questions, selecting one full question from each module.
Text Books:
- Elaine Rich And Kevin Knight,Artificial Intelligence, Tata Mcgraw-Hill,3rd edition,2008,ISBN-13: 978-0070087705
- Stuart Russel, Peter Norvig ,Artificial Intelligence- A Modern Approach, PEI,3rd edition,2015,ISBN-13: 978-9332543515
Reference Books:
- Carl Townsend, Introduction to Prolog Programming.
- Ivan Bratko( Addison-Wesley),PROLOG Programming For Artificial Intelligence PEI, 3rd edition,2002,ISBN-13: 978-8131711347.
- Clocksin and Mellish,Programming with PROLOG , Springer, 5th edition, 2003,ISBN-13: 978-3540006787
For detail syllabus of all other subjects of BE As, 2017 regulation do visit As 8th Sem syllabus for 2017 Regulation.
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