Machine Learning Fundamentals Quiz

What is the core objective of machine learning?

• To explicitly program computers to perform tasks.

• To enable computers to learn from data without being explicitly programmed.

• To create static rule-based systems.

• To only process numerical data.

Which of the following is NOT a major type of machine learning?

• Supervised learning

• Unsupervised learning

• Reinforcement learning

• Deductive learning

In supervised learning, what is the role of labeled data?

• To provide input features only.

• To provide target variables for training the model.

• To evaluate the model's performance on unseen data.

• To guide the exploration of data patterns without specific targets.

Which of the following tasks is best suited for supervised learning?

• Clustering customers into different groups based on their purchase history.

• Predicting the price of a house based on its features.

• Discovering hidden patterns in gene expression data.

• Training an agent to play a video game.

What is the primary goal of unsupervised learning?

• To predict future outcomes based on labeled data.

• To find structure and patterns in unlabeled data.

• To train an agent through rewards and penalties.

• To classify data points into predefined categories.

Which of the following is a common unsupervised learning algorithm?

• Linear Regression

• Decision Trees

• K-Means Clustering

• Support Vector Machines

Reinforcement learning involves an agent interacting with an environment to:

• Learn from explicitly labeled data.

• Discover hidden patterns in the data.

• Maximize a reward signal.

• Classify data points based on similarity.

What is a key component of a reinforcement learning system?

• Labeled dataset

• Predefined rules

• Agent, environment, and reward function

• Static model

Which of the following is an example of a reinforcement learning application?

• Image classification

• Spam detection

• Robotics control

• Customer segmentation

What is a feature in the context of machine learning?

• The output variable that the model predicts.

• An individual measurable property or characteristic of a data point.

• The algorithm used to train the model.

• The overall performance of the trained model.