Artificial Intelligence

Question 1

In Bayesian parameter estimation, the ________ distribution is often used as a prior or a real-valued quantity

Answer 1

Gaussian

Question 2

In a Bayes classifier with Gaussian class-conditional densities, the parameters that need to be estimated for each class are the _____ and ________ _______ of the Gaussian distribution

Answer 2

mean, covariance matrix

Question 3

The core principle of _________ statistics is using prior knowledge and observed data to update beliefs.

Answer 3

Bayesian

Question 4

MAP stands for ________ ___ _______ in the context of Bayesian estimation.

Answer 4

Maximum A Posteriori

Question 5

The property of Gaussian distribution that is NOT directly related to its use in the central limit theorem is the highest entropy among distributions with a given mean and variance. (True / False)

Answer 5

True

Question 6

The ________ ______ theorem states that sums of independent random variables have a Gaussian distribution.

Answer 6

central limit

Question 7

Kschischang’s Algorithm is proposed for message passing on a factor graph in _________ ________ __________.

Answer 7

Loopy Belief Propagation

Question 8

The role of a regularization term in a machine learning model is to reduce overfitting by penalizing large coefficients. (True / False)

Answer 8

True

Question 9

Training data is NOT typically a component of a Bayesian network. (True / False)

Answer 9

True

Question 10

In _______ learning, the goal is to learn a mapping from input ( X ) to output ( Y ) based on a training set of input-output pairs. This process is primarily focused on prediction.

Answer 10

supervised

Question 11

The Distribution rule is a basic rule of probability. (True / False)

Answer 11

False

The Distribution rule is NOT a basic rule of probability.

Question 12

Bayes’ theorem is a crucial concept in probabilistic machine learning. It relates the conditional and marginal probabilities of random variables.

What is the formula for Bayes’ theorem?

Answer 12

(P|A)= [P(B|A) P(A)]/P(B)

Question 13

A Gaussian distribution is also known as ______ ________.

Answer 13

Normal distribution

Question 14

The parameters that fully define a univariate Gaussian (normal) distribution are the ________ and the __________.

Answer 14

mean (μ), variance (σ²)

Question 15

The _______ distribution represents the updated belief about a parameter after observing data in Bayesian inference.

Answer 15

posterior

Question 16

Gradient Descent is a common method for approximating posterior distributions in Bayesian inference. (True / False)

Answer 16

False

Gradient Descent is NOT a common method for approximating posterior distributions in Bayesian inference.

Question 17

The conjugate prior for the Gaussian likelihood with known variance is the _________ ____________.

Answer 17

Normal distribution

Question 18

In the context of conjugate priors, a prior is termed “________” if the posterior distribution belongs to the same family of distributions as the prior distribution.

Answer 18

conjugate

Question 19

___________ __________ __________ is commonly used to estimate the parameters in linear regression.

Answer 19

Maximum Likelihood Estimation (MLE)

Question 20

In linear regression, the assumption NOT typically made about the errors (residuals) is that they are equal to zero. (True / False)

Answer 20

True

Question 21

K-means is typically used for classification tasks. (True / False)

Answer 21

False

K-means is NOT typically used for classification tasks.

Question 22

In the context of logistic regression, the ________ function maps predicted values to probabilities.

Answer 22

sigmoid

Question 23

In Hidden Markov Models (HMMs), the ‘_____’ states represent the underlying system states that are not directly observed.

Answer 23

hidden