Introduction to Machine Learning

Introduction to Machine Learning

University

10 Qs

quiz-placeholder

Similar activities

Statistik Sosial PMI

Statistik Sosial PMI

University

10 Qs

Kuis Populasi dan Sampel

Kuis Populasi dan Sampel

University

10 Qs

íaisudjdjdjd

íaisudjdjdjd

1st Grade - University

13 Qs

PROFIT LOSS DISCOUNT

PROFIT LOSS DISCOUNT

University

10 Qs

Array

Array

University

10 Qs

introduce

introduce

8th Grade - University

10 Qs

Chapter 25 Quiz

Chapter 25 Quiz

University

15 Qs

QUIZ 1 DBM30043 SESI 1 23/24

QUIZ 1 DBM30043 SESI 1 23/24

University

14 Qs

Introduction to Machine Learning

Introduction to Machine Learning

Assessment

Quiz

Mathematics

University

Practice Problem

Easy

Created by

MAHARANI BAKAR

Used 10+ times

FREE Resource

AI

Enhance your content in a minute

Add similar questions
Adjust reading levels
Convert to real-world scenario
Translate activity
More...

10 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

20 sec • 1 pt

What is the primary goal of Machine Learning?

To explicitly program rules for every possible scenario

To enable machines to learn patterns from data and make predictions

To replace traditional programming completely

To execute commands exactly as programmed

Answer explanation

The primary goal of Machine Learning is to enable machines to learn patterns from data and make predictions, rather than relying on explicitly programmed rules or commands.

2.

MULTIPLE CHOICE QUESTION

20 sec • 1 pt

In Supervised Machine Learning, what does a labeled dataset mean?

The dataset contains only numerical values

The dataset is already divided into training and testing sets

Each data point has an associated known output value

The dataset is unlabeled and unstructured

Answer explanation

In Supervised Machine Learning, a labeled dataset means that each data point has an associated known output value, which is essential for training models to make predictions.

3.

MULTIPLE CHOICE QUESTION

20 sec • 1 pt

Which of the following is NOT a type of Supervised Learning algorithm?

Decision Trees

Neural Networks

Regression Analysis

K-Means Clustering

Answer explanation

K-Means Clustering is NOT a type of Supervised Learning algorithm; it is an Unsupervised Learning method used for clustering data. In contrast, Decision Trees, Neural Networks, and Regression Analysis are all supervised techniques.

4.

MULTIPLE CHOICE QUESTION

20 sec • 1 pt

What is the key difference between Machine Learning and Traditional Programming?

ML algorithms do not require training data

Traditional programming is always more efficient than ML

ML completely eliminates the need for human intervention

Traditional programming relies on explicitly coded rules, while ML learns from data

Answer explanation

The key difference is that traditional programming uses explicitly coded rules to solve problems, while machine learning algorithms learn patterns from data, allowing them to adapt and improve over time.

5.

MULTIPLE CHOICE QUESTION

20 sec • 1 pt

Which field(s) contribute to Machine Learning?

Statistics

Linear Algebra

Both of A and B

None of them

Answer explanation

Machine Learning relies on both Statistics for data analysis and Linear Algebra for handling data structures and algorithms. Therefore, the correct answer is 'Both of A and B'.

6.

MULTIPLE CHOICE QUESTION

20 sec • 1 pt

Which of the following is a characteristic of Unsupervised Learning?

Works with labeled data

Requires a known output variable

Finds hidden patterns and structures in data

Uses regression techniques for predictions

Answer explanation

Unsupervised Learning identifies hidden patterns and structures in data without needing labeled outputs. This distinguishes it from supervised learning, which relies on known output variables.

7.

MULTIPLE CHOICE QUESTION

20 sec • 1 pt

What is the purpose of a Test Data Set in Machine Learning?

To adjust the algorithm’s parameters

To evaluate the model’s performance on unseen data

To remove unnecessary features from the dataset

To train the model

Answer explanation

The purpose of a Test Data Set in Machine Learning is to evaluate the model's performance on unseen data, ensuring it generalizes well beyond the training set.

Access all questions and much more by creating a free account

Create resources

Host any resource

Get auto-graded reports

Google

Continue with Google

Email

Continue with Email

Classlink

Continue with Classlink

Clever

Continue with Clever

or continue with

Microsoft

Microsoft

Apple

Apple

Others

Others

Already have an account?