Artifin - Module 4

Fuzzy

This refers to something which is unclear or vague.

Fuzzy Logic

This is a method of reasoning that resembles human reasoning. This approach is similar to how humans perform decision making. And it involves all intermediate possibilities between YES and NO.

Conventional logic block

The ___________ that a computer understands takes precise input and produces a definite output as TRUE or FALSE, which is equivalent to a human being’s YES or NO.

Lotfi Zadeh

The Fuzzy logic was invented by __________ who observed that unlike computers, humans have a different range of possibilities between YES and NO

Vagueness, Partial Truth

Fuzzy logic tries to capture the essential concept of __________ and capture the meaning of __________

Rules/Knowledge Base

It contains all the rules and the if-then conditions offered by the experts to control the decision-making system.

Fuzzification

This step converts inputs or the crisp numbers into fuzzy sets. You can measure the crisp inputs by sensors and pass them into the control system for further processing.

Inference Engine

It determines the degree of match between fuzzy input and the rules. According to the input field, it will decide the rules that are to be fired. Combining the fired rules, form the control actions.

Defuzzification

The ___________ process converts the fuzzy sets into a crisp value. There are different types of techniques available, and you need to select the best suited one with an expert system.

Membership function

The _______________ is a graph that defines how each point in the input space is mapped to membership value between 0 and 1. It allows you to quantify linguistic terms and represent a fuzzy set graphically.

Crisp set theory 

___________ is governed by a logic that uses one of only two values: true or false. This logic cannot represent vague concepts, and therefore fails to give the answers on the paradoxes.

Fuzzy Set Theory

The basic idea of the ___________ is that an element belongs to a fuzzy set with a certain degree of membership. Thus, a proposition is not either true or false, but may be partly true (or partly false) to any degree.

Fuzzy Variable

At the root of fuzzy set theory lies the idea of linguistic variables. A linguistic variable is a ___________.

Linguistic variable

A ___________ carries with it the concept of fuzzy set qualifiers, called hedges, which are terms that mofiy the shape of fuzzy sets

Hedges

These are terms that modify the shape of fuzzy sets; they include adverbs such as very, somewhat, quite, more or less, and slightly. They act as operations themselves

Fuzzy number

A ___________ is a fuzzy set of (real) numbers.

Machine Learning

_____________ is the science of having computers to learn and act like humans, and improve their learning over time in an autonomous manner by feeding them with data and information in the form observations and real world interactions.

Machine Learning

____________ is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. It focuses on the development of computer programs that can access data and use it learn for themselves.

Data

Machine Learning algorithms must be trained on _____. The more ______ you provide to your algorithm, the better it gets.

Deep Learning

____________ is a very young field of artificial intelligence based on artificial neural networks. It can be viewed again as a sub field of Machine Learning since it also require data in order to learn to solve tasks.

Neural Networks

Deep Learning uses a multi-layered structure of algorithms called the ___________. These have unique capabilities that enable Deep Learning models to solve tasks that Machine Learning models could never solve.

Supervised and Unsupervised Learning

Machine Learning is generally divided into two categories:

Supervised learning

In __________ you train the machine using data which is well "labeled." It means some data is already tagged with the correct answer.

Supervised Learning Algorithm

A _____________ learns from labeled training data, helps you to predict outcomes for unforeseen data. Successfully building, scaling, and deploying accurate supervised machine learning Data science model takes time and technical expertise from a team of highly skilled data scientists.

Regression

____________ algorithms are used if there is a relationship between the input variable and the output variable. It is used for the prediction of continuous variables, such as Weather forecasting, Market Trends, and others. This also means it predicts a single output value using training data.

Classification

___________ algorithm is a Supervised Learning technique that is used to identify the category of new observations on the basis of training data. In this type of technique, a program learns from the given dataset or observations and then classifies new observation into a number of classes or groups.

Binary Classification, Multi-class classification

If the algorithm tries to label input into two distinct classes, it is called ______________ . Selecting between more than two classes is referred to as _____________.

Unsupervised learning

____________ is a machine learning technique, where you do not need to supervise the model. Instead, you need to allow the model to work on its own to discover information. It mainly deals with the unlabelled data.

Clustering

___________ is an important concept when it comes to unsupervised learning. It mainly deals with finding a structure or pattern in a collection of uncategorized data. It is a method of grouping the objects into clusters such that objects with most similarities remains into a group and has less or no similarities with the objects of another group.

Cluster analysis

__________ finds the commonalities between the data objects and categorizes them as per the presence and absence of those commonalities.

Association

____________ allow you to establish associations amongst data objects inside large databases. This unsupervised technique is about discovering exciting relationships between variables in large databases. For example, people that buy a new home most likely to buy new furniture