Assessment due date is $2024-10-02,23$:$59$ IST

Last recorded submission : Assessment not submitted

Each question carries $01$ Mark each. There are MORE than ONE correct options for some of the Questions. All correct options must be identified for the answer to be evaluated as correct.

$1$ point

Of the two strands of Artificial Intelligence activity, the one that seeks to understand how intelligent behaviour arises is the $\_\_\_\_\_\_\_\_\_\_\_\_\_\_.$

A$.$ Cognitive approach.

B$.$ Engineering approach.

C$.$ Machine Learning approach.

D$.$ Symbolic approach.

$1$ point

$\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_$ is the approach for modeling the relationship between a scalar dependent variable Y and one or more explanatory variables $($or independent variables$)$ denoted X$.$

A$.$ Regression

B$.$ Classification

C$.$ Clustering

D$.$ Matching

$1$ point

In pure inductive inference, given a collection of examples of f$,$ return a function h that approximates f$.$ The function h is called a $\_\_\_\_\_\_\_\_\_\_\_\_\_.$

A$.$ utility function.

B$.$ hypothesis

C$.$ approximation function

D$.$ classifier

$1$ point

Which of the following statements are correct for Machine Learning vis$--$vis Statistics?

I. Machine learning deals with low$-$dimensional data $[$e$.$g$.$ less than $100$ D$].$

II$.$ Structure in the data; methods involve figuring out a way to represent this structure.

A$.$ Statements I and II

B$.$ Only Statement II

C$.$ Only Statement I

D$.$ None.

$1$ point

A decision$-$tree learning system for real$-$world applications must be able to handle the following problems:

A$.$ Missing Data

B$.$ Approximation

C$.$ Continuous$-$valued attribute

D$.$ Multivalued attribute

$1$ point

Assertion: For implementing choose$-$attribute in DTL $($Decision Tree Learning$)$ algorithm, one exploits Information Theory to measure the expected amount of information provided by the attribute.

Reason: Information Theory provides a mathematical basis for measuring the information content.

Mark the correct choice as

A$.$ Both A and R are true and R is the correct explanation for A

B$.$ Both A and R are true but R is not the correct explanation for A

C$.$ A is True but R is False

D$.$ A is false but R is True

$1$ point

Assertion: Overfitting makes use of irrelevant attributes to distinguish between samples that have no meaningful differences.

Reason: The presence of irrelevant attributes may lead to more degrees of freedom in the decision tree.

Mark the correct choice as

A$.$ Both A and R are true and R is the correct explanation for A

B$.$ Both A and R are true but R is not the correct explanation for A

C$.$ A is True but R is False

D$.$ A is false but R is True

$1$ point

Assertion: All knowledge accrues through a process of learning.

Reason: Learning refers to the capability of autonomous acquisition and integration of knowledge.

Mark the correct choice as

A$.$ Both A and R are true and R is the correct explanation for A

B$.$ Both A and R are true but R is not the correct explanation for A

C$.$ A is True but R is False

D$.$ A is false but R is True

$1$ point

For a learning agent $\_\_\_\_\_\_\_\_\_\_$ provides ways and means of weighing up the desirability of goals and the likelihood of achieving them.

A$.$ Decision Theory

B$.$ Utility Theory

C$.$ Probability Theory

D$.$ Bayesian Networks

$1$ point

Although the three ML paradigms $$ Supervised, Unsupervised and Reinforcement Learning help to organize ideas; much current research involves blends across these categories. $\_\_\_\_\_\_\_\_\_\_$learning makes use of unlabeled data to augment labeled data in a supervised learning context.

A$.$ Active

B$.$ Unsupervised

C$.$ Semisupervised

D$.$ Reinforcement

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