Mechatronics

Artificial Intelligence Mechatronics 8th Sem Syllabus for VTU BE 2017 Scheme (Professional Elective-V)

Artificial Intelligence detail syllabus for Mechatronics (Mechatronics), 2017 scheme is taken from VTU official website and presented for VTU students. The course code (17MT832), 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 mechatronics 8th sem syllabus for be 2017 scheme vtu you can visit Mechatronics 8th Sem syllabus for BE 2017 Scheme VTU Subjects. For all other Professional Elective-V subjects do refer to Professional Elective-V. The detail syllabus for artificial intelligence is as follows.

Course Objectives:

Students will be able to

  • Gain Knowledge of Artificial Intelligence, Production Rules, Search Algorithms, Expert System & its architectures, Machine Learning.
  • Understand the working methodology of Search Algorithms, Expert System & Machine Learning.

Module 1

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

Module 2

Space Representation: Defining the Problem, Production Rules for water jug problem, Breadth-First Search Algorithm , Depth-First Search Algorithm, Generate & Test Algorithm, Hill Climbing Algorithms: Simple Hill Climbing Algorithm, Steepest-Ascent Hill Climbing Algorithm.

Module 3

Expert Systems: Introduction, Characteristics of Expert System, Need of an Expert System, Expert System Architecture, Steps to develop an Expert System ,case studies: MYCIN ,DENDRAL. and Neural Nets: Introduction ,TAN-Toy Adaptive Node ,Network Structures, Application of Neural Nets.

Module 4

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

Module 5

Introduction to Machine Learning: Introduction, Perceptrons, Perceptron Learning Algorithm, Checkers Playing Examples, Learning automata:Automaton model, Temperature Control Model, CLA representation of NIM game, Genetic Algorithms, Intelligent editors .

Course Outcomes:

On completion of course students will

  • have Knowledge of Artificial Intelligence, Production Rules, Search Algorithms, Expert System & its architectures, Machine Learning.
  • understand the working methodology of Search Algorithms, Expert System & Machine Learning.

Text Books:

  1. Artificial Intelligence, Elaine Rich & Kevin Knight, M/H 2004.
  2. Introduction to AI & ES, Dan W. Patterson, Prentice Hall of India, 2012.
  3. Artificial Intelligence A Practical Approach, Er.Rajiv Chopra, S.Chand & Company Ltd,2012.

Reference Books:

  1. Principles of Artificial intelligence, Springer Vertag, Berlin, 1981.
  2. Artificial intelligence in business, Science & Industry, Wendy B, Ranch
  3. A guide to Expert systems, Waterman, D. A. Addison – Wesley inc. 1986.
  4. Building Expert Systems, Hayes, Roth, Waterman, D. A. Addison Wesley, 1983.

For detail syllabus of all other subjects of BE Mechatronics, 2017 regulation do visit Mechatronics 8th Sem syllabus for 2017 Regulation.

Dont forget to download iStudy for latest syllabus and results, class timetable and more.

Leave a Reply

Your email address will not be published. Required fields are marked *

*