# NPTEL Data Science for Engineers Assignment 4 Answers 2022

Are you looking for the Answers to NPTEL Data Science for Engineers Assignment 4? This article will help you with the answer to the National Programme on Technology Enhanced Learning (NPTEL) Course NPTEL Data Science for Engineers Assignment 4

## What is Data Science for Engineers?

Data Science for Engineers is a fun-filled course where Domain Certification helps learners to gain expertise in a specific Area/Domain. This can be helpful for learners who wish to work in a particular area as part of their job or research or for those appearing for some competitive exam or becoming job-ready or specialising in an area of study.

Every domain will comprise Core courses and Elective courses. Once a learner completes the requisite courses per the mentioned criteria, you will receive a Domain Certificate showcasing your scores and the domain of expertise.

## CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of the average of best 6 assignments out of the total 8 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF THE AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Below you can find the answers for NPTEL Data Science for Engineers Assignment 4

## NPTEL Data Science for Engineers Assignment 4 Answers:-

Q1. If f(x)=2x3+3x2−12x+1, then the first order necessary condition for either maxima or minima of f(x) is

Q2. For the function f(x)=2x3+3x2−12x+1, which of the following points are stationary point(s) of f(x)?

Q3. For the function f(x)=2x3+3x2−12x+1, the stationary point which qualifies to maximize the value of f(x) is

Q4. For the function f(x)=2x3+3x2−12x+1, the stationary point which qualifies to minimize the value of f(x) is

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Q5. Gradient descent is an iterative optimization algorithm for finding the local minimum of a function

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Q6. Different types of optimization problems are based on

Q7. The maximization of a function f(x¯) is equal to the minimization of the function