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Mockinsem

1. The document is a mock exam for a Machine Learning course, covering topics from Units 1-3, with 6 multiple choice questions. 2. The questions assess students on concepts like adaptive machines, machine learning applications, how machine learning works for unsupervised learning tasks, feature engineering, managing categorical data in classification, creating training and test sets, linear classification algorithms, and types of regression. 3. Instructions provide that the exam is based on the first three units of the course, to draw neat diagrams, and assume suitable data if needed. It also lists the course outcomes related to distinguishing learning applications, preprocessing data, and designing/implementing algorithms.

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sabitha s
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55 views1 page

Mockinsem

1. The document is a mock exam for a Machine Learning course, covering topics from Units 1-3, with 6 multiple choice questions. 2. The questions assess students on concepts like adaptive machines, machine learning applications, how machine learning works for unsupervised learning tasks, feature engineering, managing categorical data in classification, creating training and test sets, linear classification algorithms, and types of regression. 3. Instructions provide that the exam is based on the first three units of the course, to draw neat diagrams, and assume suitable data if needed. It also lists the course outcomes related to distinguishing learning applications, preprocessing data, and designing/implementing algorithms.

Uploaded by

sabitha s
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as XLS, PDF, TXT or read online on Scribd
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SNJB's Late Sau.

Kantabai Bhavarlalji Jain College of Engineering


Department : Computer Engineering
Academic Year : 2018-19 (Semester II)
Mock In-Sem Exam
Class : B.E. Marks :30 Date:25/2/2019 Time :2:30pm to 3:30 pm Duration : 1 Hr.
Course : Machine Learning (ML)
Instructions:
1. Mock In Sem Exam based on Unit-I,II and III
2. Draw neat and clean diagram
3. Assume suitable data if necessary
Mar Unit Marking Scheme
CO PI SBLOOM
Level QN Questions
ks No.
Code (1-6)

1 2 Q1 A) Explain the concept of adaptive machines with reference to machine 4 1 Valid 4 Points-4M
learning,
1 2 Q1 B) What does Machine learning exactly mean? Explain Application of 6 1 ML-2M
Machine Learning for data scientists. Application-4M

OR
1 2 Q2 A) How machine Learning works for Big data applications? 4 1 Application- 4M
1 2 Q2 B) Explain how machine learning works for the following common un- 6 1 2M for each
supervised learning applications: Application
I. Object segmentation (for example, users, products, movies, songs,
and so on)
II. Similarity detection
III. Automatic labeling

Justify the statement: Feature engineering is the first step in a 4 2 Justification-4M


machine learning pipeline and involves all the techniques adopted to
2 3 Q3 A) clean existing datasets.

2 2 Q3 B) How categorical data are Managed in various classification problems? 6 2 Categorial data-2M
Classification
Problem-4 M

OR
Explain the process of Creating training and test sets for Iris Dataset. 4 2 Training sets-2M
2 3 Q4 A) Test sets-2 M
2 2 Q4 B) Write a short note on Sparse PCA and Kernel PCA 6 2 2M for each
Explain linear classification algorithm with example. 4 3 Problem-2M Example-
3 2 Q5 A) 2M
6 3 ROC-4M Logistic-2M
3 2 Q5 B) What is significance of ROC curve with reference to logistic
regression?
OR

3 2 Q6 A) Compare linear and logistic regression? 4 3 4 Valid Point-4M


3 2 Q6 B) Explain the following types of regression with Examples. 6 3 1M each type 1M each
1.Ridge 2.Lasso type example
3.ElasticNet.

CO Course Outcome
4E+05
1 C410.1 Distinguish different learning based applications
2 C410.2 Apply different preprocessing methods to prepare training data set for machine learning.
3 C410.3 Design and Implement supervised and unsupervised machine learning algorithm.

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