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BRI405B

This document is a model question paper for the Fourth Semester B.E. Degree Examination in Machine Learning Fundamentals. It includes various questions categorized into five modules, covering topics such as Artificial Intelligence applications, machine learning types, regression, classification, clustering algorithms, and reinforcement learning. Students are required to answer any five full questions, ensuring at least one question from each module.

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bicchunavermehaboob
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249 views2 pages

BRI405B

This document is a model question paper for the Fourth Semester B.E. Degree Examination in Machine Learning Fundamentals. It includes various questions categorized into five modules, covering topics such as Artificial Intelligence applications, machine learning types, regression, classification, clustering algorithms, and reinforcement learning. Students are required to answer any five full questions, ensuring at least one question from each module.

Uploaded by

bicchunavermehaboob
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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BRI405B

Model Question Paper-I


Fourth Semester B.E. Degree Examination
Machine Learning Fundamentals
TIME: 03 Hours Max. Marks: 100
Note: 01. Answer any FIVE full questions, choosing at least ONE question from each
module

*Bloom’s
QNo Module -1 Taxonomy Marks
Level
Q1 a List and Explain the applications of Artificial L2
Intelligence (AI). 10
b Define Machine learning. Explain the different types of L3 10
Machine learning.
OR
Q2 a With a neat diagram , Explain the Machine learning L3 10
workflow.
b Explain the concepts of NumPy , Pandas and Matplotlib L2 10
with an example.
Module -2
Q3 a Explain Regression and Classification with an example. L2 10
b What is Linear Regression and Logistic Regression? L3 10
Explain with an example.
OR
Q4 a Explain Decision tree with an example. L2 10
b Explain Random forest with an example. L2 10
Module -3
Q5 a How to implement Support Vector Machine (SVM) in L3 10
Machine learning.
b Explain the concept of K-Nearest Neighbour (KNN) in L2 10
Machine learning.
OR
Q6 a Explain Naïve Bayes Classifier with an Example. L2 10
b List and Explain different Evaluation Metrics in L2 10
Machine Learning.
Module -4
Q7 a Explain the different types of clustering algorithm used L2 10
in Machine learning.
b Define PCA ? How does the Principal component L3 10
analysis work.
OR
Q8 a Explain the Dimensionality reduction techniques. L2 10
BRI405B

b Explain the Association rule mining and Anomaly L2 10


detection.
Module -5
Q9 a Define Reinforcement learning ? What are the L2 10
applications of Reinforcement learning in detail.
b How Markov decision processes works in Machine L3 10
learning.
OR
Q 10 a Explain how Markov Q-Learning works in L2 10
Reinforcement Learning
b What is Deep Reinforcement learning? Explain the two L3 10
classes of Dynamic programming.

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