What is machine learning?
Q: What is machine learning?
A: Machine learning is a subfield of computer science that gives computers the ability to learn without being explicitly programmed, using algorithms that can learn and make predictions on data.
Q: Where did the idea for machine learning come from?
A: The idea for machine learning came from work in artificial intelligence.
Q: How do algorithms used in machine learning work?
A: Algorithms used in machine learning follow programmed instructions, but can also make predictions or decisions based on data. They build a model from sample inputs.
Q: When is machine learning used?
A: Machine learning is used where designing and programming explicit algorithms cannot be done. Examples include spam filtering, detection of network intruders or malicious insiders working towards a data breach, optical character recognition (OCR), search engines and computer vision.
Q: What are some risks of using machine learning?
A: Using machine learning has risks, including the creation of final models that are "black boxes" and criticized for biases in hiring, criminal justice, and recognizing faces.
Q: What does it mean for a machine learning model to be a "black box"?
A: A "black box" machine learning model means that its decision-making processes are not easily explainable or understandable by humans.
Q: What are some examples of applications of machine learning?
A: Some examples of applications of machine learning include spam filtering, detection of network intruders, optical character recognition (OCR), search engines, and computer vision.
