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Fuzzy Logic

This course offers the fundamentals of fuzzy logic and its application in real world.

Instructor: Ms. Anushka Joshi

Class Area: COER

Email: anushkazinc@gmail.com

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ANNOUNCEMENTS

Feburary 22, 2024: Classes will begin

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SYLLABUS (Click on the text to view syllabus)

EVALUATION COMPONENTS

  • Term tests/quizzes (10%)

  • Assignments + Practical (15%)

  • Class participation (5%)

  • Mid-Term Examination (30%)

  • End Term Examination (40%)

COURSE OBJECTIVES, LEARNING OUTCOMES AND PREREQUISITES

  • To understand the basic concepts of Fuzzy Logic

  • To use python for solving real world problems involving Fuzzy Logic

Prerequisites: Set Theory and Basics of Python

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LECTURE NOTES

  1. Introduction to Fuzzy Logic : Link

  2. Algorithms Perceptions: Link
  3. Backpropagation and Multi-Layer Perceptron: Link
  4. Hopfield Networks and Application of AI: Link
  5. Fuzzy Logic Introduction: Link
  6. Fuzzy Logic Explain: Link
  7. Fuzzy Logic Operations: Link

EXAMINATIONS​

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PRACTICE QUIZ

1. Practice Quiz 1:  Link

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ASSIGNMENT

Submission Intructions

1. Assignment 1:

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MTE Question Papers

1. MTE 1: 

2. MTE 2: To be conducted...

RECOMMENDED STUDY MATERIAL

​The following will be used as a reference/textbook for this course:

  1. Franck Dernoncourt, "Introduction to fuzzy logic", MIT, 2013 (Link)

  2. Professor H.R.Tizhoosh, SYDE 522 – Machine Intelligence (Winter 2019, University of Waterloo)

  3. Ian Goodfellow and Yoshua Bengio and Aaron Courville, "Deep Learning", MIT Press, 2016

  4. Christopher M. Bishop, "Pattern Recognition and Machine Learning", 2006

  5. Tom Mitchell, "Machine Learning", 1997

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