Chapter 1_Introduction to Machine Learning Quiz

Which of the following best describes Machine Learning?

• The science of programming computers to learn from data.

• The process of manually coding every possible rule for a computer.

• The study of human learning and memory.

• The use of computers only for arithmetic calculations.

Answer explanation

The correct choice, 'The science of programming computers to learn from data,' accurately defines Machine Learning, which focuses on algorithms that improve through experience rather than manual coding of rules.

According to Arthur Samuel, what is the field of study that gives computers the ability to learn without being explicitly programmed?

• Artificial Intelligence

• Machine Learning

• Data Mining

• Robotics

Answer explanation

Arthur Samuel defined Machine Learning as the field that enables computers to learn from data and improve their performance without being explicitly programmed, distinguishing it from broader Artificial Intelligence.

Which of the following is the first step in the Machine Learning approach as shown in Figure 1-2?

• Train ML algorithm

• Study the problem

• Analyze errors

• Launch!

Answer explanation

The first step in the Machine Learning approach is to 'Study the problem'. This involves understanding the context, requirements, and objectives before proceeding to train algorithms or analyze errors.

Which branch of NLP is used for summarizing long documents automatically?

• Text classification

• Text summarization

• Semantic segmentation

• Image classification

Answer explanation

Text summarization is the branch of NLP specifically designed to condense long documents into shorter summaries, making it the correct choice for this question.

What type of machine learning task is detecting credit card fraud?

• Anomaly detection

• Clustering

• Regression

• Reinforcement learning

Answer explanation

Detecting credit card fraud is an anomaly detection task because it involves identifying unusual patterns or behaviors in transaction data that deviate from the norm, indicating potential fraud.

Which of the following best describes instance-based learning?

• Comparing new data points to known data points in the training data

• Detecting anomalies in data

• Training agents to maximize rewards

• Reducing the dimensionality of data

Answer explanation

Instance-based learning involves comparing new data points to known data points in the training data to make predictions. This approach relies on the similarity between instances rather than a global model.

Which of the following is an example of a supervised learning algorithm?

• k-Nearest Neighbors

• Clustering

• Principal Component Analysis

• Association Rule Learning

Answer explanation

k-Nearest Neighbors (k-NN) is a supervised learning algorithm because it uses labeled data to classify new instances based on the closest training examples. The other options are unsupervised learning methods.

What is the primary purpose of anomaly detection in unsupervised learning?

• To find the most common patterns in the data

• To detect unusual or rare instances that differ from normal patterns

• To classify data into predefined categories

• To increase the number of anomalies in the dataset

Answer explanation

The primary purpose of anomaly detection in unsupervised learning is to identify unusual or rare instances that differ from normal patterns, helping to uncover insights that may not be apparent in typical data.

In semisupervised learning, how can unlabeled examples (circles) help classify a new instance?

• They help determine the class of a new instance by their distribution, even if it is closer to another class

• They are ignored in the classification process

• They are always labeled as a separate class

• They are used only for anomaly detection

Answer explanation

In semisupervised learning, unlabeled examples help by providing context about the distribution of data. This allows the model to classify new instances based on their proximity to these examples, even if they seem closer to another class.

Which of the following best describes the role of an agent in Reinforcement Learning?

• The agent observes the environment, selects and performs actions, and receives rewards or penalties in return.

• The agent only observes the environment without taking any actions.

• The agent provides rewards to other systems.

• The agent is responsible for labeling data for supervised learning.

Answer explanation

The correct choice describes the agent's role in Reinforcement Learning accurately. The agent interacts with the environment by observing it, taking actions, and receiving feedback in the form of rewards or penalties.