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COMSATS UNIVERSITY
ISLAMABAD,
ABBOTTABAD CAMPUS
Object oriented softwaere engineering
Assignment no# 4
DATE: 13 JUNE 2021
Topic of Assignment:
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Summary of research paper
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TITLE:
Studying Software Engineering
Patterns for Designing Machine
Learning Systems
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What is Machine learning:
Machine learning is a method of data analysis that automates analytical model
building. It is a branch of Artificial intelligence based on the idea that systems can
learn from data, identify patterns and make decisions with minimal human
intervention.
Why is machine learning important?
Resurging interest in machine learning is due to the same factors that have made
data mining and Bayesian analysis more popular than ever. Things like growing
volumes and varieties of available data, computational processing that is cheaper
and more powerful, and affordable data storage.
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All of these things mean it's possible to quickly and automatically produce models
that can analyze bigger, more complex data and deliver faster, more accurate
results – even on a very large scale. And by building precise models, an
organization has a better chance of identifying profitable opportunities or
avoiding unknown risks.
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What's required to create good machine learning systems?
 Data preparation capabilities.
 Algorithms – basic and advanced.
 Automation and iterative processes.
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 Scalability.
 Ensemble modeling.
 In machine learning, a target is called a label.
 In statistics, a target is called a dependent variable.
 A variable in statistics is called a feature in machine learning.
 A transformation in statistics is called feature creation in machine learning.
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SUMMARY OF THE RESEARCH
PAPER:
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The ubiquity of Machine-learning (ML) techniques has expanded lately. They
are being utilized in numerous areas, including network protection, IoT, and
self-sufficient vehicles. ML techniques depend on science and computer
programming. Science is utilized to create the calculations, foster abilities to
gain from input information, and produce agent models. Then again,
programming is utilized for execution and hearty execution. Albeit numerous
works have explored the science also, software engineering on which ML
techniques are fabricated, few have analyzed their execution, which raises
numerous worries. The first is the product intricacy of ML techniques. The
second is the nature of the accessible executions, including execution and
dependability. The third is the nature of the models, which might be adversely
affected by a product bug. These worries could be eased if developers could
show the product nature of their executions of the ML techniques. Therefore,
specialists and experts have been concentrating best practices to design ML
frameworks furthermore, programming to resolve issues with programming
intricacy and nature of ML techniques. Such practices are frequently formalized
as engineering patterns and design patterns by epitomizing reusable answers
for normally happening issues inside the given settings in ML frameworks and
programming design.— Machine-learning (ML) techniques are turning out to
be more pervasive. ML techniques depend on math and programming.
Analysts and professionals concentrating best rehearse endeavor to design ML
frameworks and programming that location programming intricacy and quality
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issues. Such design rehearses are frequently formalized as engineering and
design patterns by typifying reusable answers for normal issues inside given
settings. Nonetheless, a precise report to gather, characterize, also, examine
these computer programming (SE) design patterns for ML techniques still can't
seem to be accounted for. This gathers great/terrible SE design patterns for ML
techniques to give developers an exhaustive characterization of such patterns.
In this report the fundamental consequences of a precise writing audit (SLR) of
good/terrible design patterns for ML.
DISCUSSIONS We describe succinctly two extracted patterns. We omit
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for the sake of brevity: participants, collaborations, implementation, sample
code, and known uses. We then discuss threats to the validity of our results .
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