Data Science Competitions/Seminars/Fora/Courses 2020-05-15T15:43:09+00:00

# Courses

## Introduction to Fuzzy Set Theory, Arithmetic and Logic

Excerpt: “In most real-life applications of any decision making, one needs to face many types of uncertainty. As humans, we can deal with this uncertainty with our reasoning prowess. However, it is not clear how to deal with this uncertainty in a system. Fuzzy sets and fuzzy logic give us one way of representing this uncertainty and reasoning with them.”

Topics covered:

• Fuzzy sets, crisp vs. fuzzy types of fuzzy sets, membership functions, alpha cuts
• Operation on fuzzy sets, t-norm, complements t-conorm, combination of operations
• Fuzzy arithmetic interval, decomposition principle, extension principle, fuzzy equations
• Relations, fuzzy relations projections, equivalence relation, transitive closure, compatibility relation
• Propositional logic, Boolean algebra multi-valued logic
•  Inference from conditional and qualified fuzzy propositions
•  Fuzzy quantifiers, inference from quantified fuzzy propositions
•  Possibility vs. probability, belief and plausibility, Dempster’s rule

## Udemy: Practical Introduction to Fuzzy Logic with Matlab

Topics covered:

• Fuzzy Logic Foundations: Fuzzy sets and membership functions; operations on fuzzy sets; union and intersection
• Fuzzy Control vs. Classical Control: Necessity of fuzzy logic
• Fuzzy Arithmetic Functions: discrete fuzzy function, continuous fuzzy function
• Fuzzy Inference Systems and Fuzzy Rules: fuzzy linguistic variables, fuzzy ruled based systems

## Fundamentals of Fuzzy Logic

Excerpt: “FL is a form of many-valued logic. It extends the truth values to an arbitrary degree of truth, formally a value in the interval [0, 1]. The aim of FL is to mimic human reasoning in an environment of uncertainty and imprecision (such as the real world). FL provides an intuitive approach to modelling human intelligence in machines, as it uses high-level linguistic inference.”

Topics covered:

• Motivations of fuzzy logic
• Linguistic uncertainty
• Fuzzy set theory
• Fuzzy logic systems
• Applications of fuzzy logic systems

## NPTEL: Fuzzy Logic and Neural Networks

Topics covered:

•  Fuzzy sets and their applications
•  Fuzzy reasoning and optimization
• Neuro-Fuzzy systems
•  Soft computing

# Lecture Notes & Seminars

## Fuzzy Logic and Systems

Topics covered:

• Fuzzy logic
• Fuzzy sets and systems
• Fuzzy knowledge-based systems

## Introduction to Fuzzy Logic

Topics covered:

• Fuzzy concepts
• Fuzzy propositional and predicate logic
• Fuzzification
• Defuzzification
• Fuzzy control systems
• Types of fuzzy algorithms
• Applications of fuzzy logic

## Applied Logic – Fuzzy Logic

Topics covered:

• Fuzzy concepts and fuzzy sets
• Operations and linguistic rules
• Fuzzy intersection (T-norm)
• Fuzzy union (T- co-norm)
• Fuzzy complement
• Duality of T-norms and S-norms
•  Fuzzy relations
• Cartesian products of fuzzy sets
• Cylindrical extension and projection
• Fuzzy logical connectives
• Fuzzy implication
• Possibilistic fuzzy logic
• Fuzzy knowledge bases
• Fuzzy theories
• Fuzzy inference

## Knowledge-Based Control Systems

Topics covered:

• Fuzzy sets and set-theoretic operations
• Fuzzy relations
• Fuzzy systems
• Linguistic model
• Approximate reasoning
• Singleton and Takagi–Sugeno fuzzy system
• Knowledge-based fuzzy modelling
•  Direct fuzzy control
• Supervisory fuzzy control

## Neural Fuzzy Systems Lecture Notes

Topics covered:

• Fuzzy sets and set-theoretic operations
•  Operations on fuzzy sets
•  Fuzzy relations
•  Fuzzy implications
•  Theory of approximate reasoning
• Fuzzy rule-based systems
• Fuzzy reasoning schemes
• Fuzzy logic controllers
•  Neuro-fuzzy classifiers

Technical articles are published from the Absolutdata Labs group, and hail from The Absolutdata Data Science Center of Excellence. These articles also appear in BrainWave, Absolutdata’s quarterly data science digest.

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