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Artificial Intelligence (AI) GLOW Class quizz

Authored by istiqomah iskak

Computers

University

Used 3+ times

Artificial Intelligence (AI) GLOW Class quizz
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30 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

In machine learning, the condition in which the model is too simple to describe the data or to learn the structure of the data is called?

Underfitting

Overfitting

Unfitting

Bad fitting

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

The main difference between supervised learning and unsupervised learning is that supervised learning requires little data to conduct training, while unsupervised learning requires a lot of data.

True

False

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

When the target variable that we are going to predict has a discrete value, the method in machine learning is called

Discretisation

Classification

Supervision

Regression

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

The key difference between Logistic Regression and Adaline rule is that (choose the right answer below):

Logistic regression updates the weights based on a linear activation function rather than a unit step function like in the Adaline

Logistic regression updates the weights based on a sigmoid function rather than a unit step function like in the Adaline

Logistic regression updates the weights based on a sigmoid function rather than an identity function like in the Adaline

Logistic regression updates the weights based on an identity function rather than an a sigmoid function like in the Adaline

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

One of the key ingredients of supervised machine learning algorithms is a defined objective function that will be used during learning process. What is the objective function that we want to maximize in Logistic Regression:

Sum of squared errors

Mean absolute errors

The logit function

The log-likelihood

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

The correct sequence of the gradient descent (GD) algorithm in terms of the amount of training data processed for each iteration, from the smallest to the largest is:

Batch GD, Mini-Batch GD, Stochastic Mini-Batch GD

Stochastic GD, Batch GD, Mini-Batch GD

Stochastic GD, Mini-Batch GD, Batch GD

Mini-Batch GD, Batch GD, Stochastic GD

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

The stochastic gradient descent method has a higher probability of reaching the global minimum compared to batch gradient descent.

True

False

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