Machine Learning: Revision Questions and Answers
Introduction to Machine Learning
Questions
1. What is machine learning?
2. Can you explain supervised learning?
3. What is unsupervised learning?
4. How does reinforcement learning work?
5. What are neural networks?
6. What is the difference between classification and regression?
7. How do you handle missing or corrupted data in a dataset?
8. What is ‘training’ in the context of machine learning?
9. What are feature vectors?
10. What is the ‘curse of dimensionality’?
11. How do you ensure your model is not overfitting?
12. What is cross-validation?
13. Can you explain the concept of ‘deep learning’?
14. What are the common challenges in machine learning?
15. How is machine learning used in image recognition?
16. What is natural language processing (NLP)?
17. What are the ethical considerations in machine learning?
18. How do you evaluate the performance of a machine learning model?
19. What is the role of data preprocessing in machine learning?
20. What are the latest trends in machine learning research?
Answers
1. Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to ‘learn’ from data, without being explicitly programmed.
2. Supervised learning involves training a model on a labeled dataset, where the correct output is known, to make predictions or decisions.
3. Unsupervised learning deals with unlabeled data, and the system tries to learn the patterns and structure from such data.
4. Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing certain actions and receiving rewards or penalties.
5. Neural networks are a series of algorithms that mimic the operations of a human brain to recognize relationships between vast amounts of data.
6. Classification is used to predict discrete responses, and regression is used for continuous responses.
7. Missing or corrupted data can be handled by various methods such as deletion, imputation, or using algorithms that support missing values.
8. ‘Training’ refers to the process of teaching a machine learning model to make predictions or decisions, typically by feeding it a large amount of data.
9. Feature vectors are an n-dimensional vector of numerical features that represent some object in a mathematical model.
10. The ‘curse of dimensionality’ refers to various phenomena that arise when analyzing and organizing data in high-dimensional spaces.
11. To prevent overfitting, one can use methods like cross-validation, regularization, or pruning.
12. Cross-validation is a technique for assessing how the results of a statistical analysis will generalize to an independent data set.
13. Deep learning is a subset of machine learning in which multi-layered neural networks learn from vast amounts of data.
14. Common challenges include dealing with unstructured data, maintaining privacy and security, and handling computational complexity.
15. Machine learning is used in image recognition to identify patterns and features in images for classification or detection.
16. NLP is a field of machine learning focused on the interaction between computers and humans through natural language.
17. Ethical considerations include bias, fairness, transparency, and the impact on employment.
18. The performance of a model can be evaluated using metrics like accuracy, precision, recall, and F1 score.
19. Data preprocessing involves cleaning and converting raw data into a suitable format for analysis.
20. Latest trends include advancements in deep learning, reinforcement learning, and the application of machine learning in new areas like healthcare and autonomous vehicles.
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