Types of Machine Learning Supervised vs Unsupervised Learning Answers 1. What is a primary characteristic of semi-supervised learning? A. It only focuses on the structure of labeled data. B. It requires only labeled data for training. C. It combines labeled and unlabeled data for training. D. It relies solely on unsupervised learning techniques. Answer: It combines labeled and unlabeled data for training. (C) Semi-supervised learning leverages both labeled and unlabeled data, enhancing training when labeled data is scarce. 2. Which technique is NOT associated with semi-supervised learning? A. Co-training B. Generative models C. Self-training D. Image classification with labels only Answer: Image classification with labels only (D) Image classification with labels only does not utilize the principles of semisupervised learning. 3. How do deep learning algorithms enhance their functionality? A. They solely rely on unsupervised learning techniques. B. They use artificial neural networks with multiple layers. C. They eliminate the need for data preprocessing. D. They only work with labeled datasets. Answer: They use artificial neural networks with multiple layers. (B) Deep learning uses layered artificial neural networks to learn from raw data and extract features. 4. Why is semi-supervised learning particularly useful? A. It improves performance with limited labeled data. B. It requires an equal amount of both labeled and unlabeled data. C. It maximizes the use of labeled data only. D. It can function without any data. Answer: It improves performance with limited labeled data. (A) Semi-supervised learning is designed to enhance performance when labeled data is limited or costly. 5. What type of problems are deep learning models particularly proficient at solving? A. Low-dimensional tasks. B. High-dimensional tasks such as image classification. C. Simple pattern recognition. D. Tasks requiring manual feature extraction. Answer: High-dimensional tasks such as image classification. (B) Deep learning excels in high-dimensional tasks by automatically extracting complex features from raw input. 6. What type of data do supervised learning algorithms utilize? A. Randomized data B. Labeled data C. Unlabeled data D. Structured data only Answer: Labeled data (B) Supervised learning algorithms require labeled data to learn from the inputoutput relationships. 7. Which of the following tasks are NOT commonly associated with supervised learning? A. Classification B. Regression C. Time series forecasting D. Clustering Answer: Clustering (D) Clustering is typically an unsupervised learning task, while classification and regression are supervised tasks. 8. Which algorithm is primarily used in classification tasks? A. Logistic regression B. Linear regression C. Principal component analysis D. K-means clustering Answer: Logistic regression (A) Logistic regression is a classification algorithm used to predict categorical outcomes. 9. What characterizes unsupervised learning algorithms? A. They seek hidden patterns in unlabeled data. B. They work with labeled data. C. They rely on predefined categories. D. They require specific input-output mapping. Answer: They seek hidden patterns in unlabeled data. (A) Unsupervised learning algorithms analyze unlabeled data to discover patterns without predefined categories. 10. Which of the following is an application of reinforcement learning? A. Stock market prediction B. Weather forecasting C. Customer segmentation D. Game playing Answer: Game playing (D) Reinforcement learning is applied in environments such as game playing, where agents learn optimal strategies. 11. In reinforcement learning, what does the reward signal indicate? A. The total amount of data available. B. The desirability of the actions performed. C. The success rate of previous actions. D. The characteristics of the environment. Answer: The desirability of the actions performed. (B) The reward signal in reinforcement learning measures the desirability of actions taken by the agent. 12. Which type of learning seeks to map inputs to outputs based on patterns found in data? A. Unsupervised learning B. Semi-supervised learning C. Supervised learning D. Reinforcement learning Answer: Supervised learning (C) Supervised learning specifically aims to create a mapping from input features to output predictions. 13. Which technique would be classified as dimensionality reduction in unsupervised learning? A. Logistic regression B. Support vector regression C. Principal component analysis D. Hierarchical clustering Answer: Principal component analysis (C) Principal component analysis (PCA) is a well-known algorithm used in dimensionality reduction.