Final answer:
A smart speaker applies unsupervised learning for De-noising to filter noise, supervised learning for Anomaly detection in fraud prevention, and unsupervised or semi-supervised learning for Density estimation in recommendation systems, enhancing user experience, security, and personalization.
Step-by-step explanation:
A daily life application that applies machine learning techniques such as De-noising, Anomaly detection, and Density estimation is a digital assistant like a smart speaker (e.g., Amazon Echo). These devices use De-noising to improve voice recognition by filtering out background noise, which is an application of unsupervised learning. Anomaly detection is used in fraud detection systems, which constantly analyze transaction patterns to identify unusual behaviors that may indicate fraudulent activity; this falls under supervised learning. Density estimation might be seen in recommendation systems that analyze user behavior to predict interests, and this can be framed either as unsupervised learning or semi-supervised learning, depending on whether labeled data are used in conjunction with unlabeled data for model training.
The reasons why these machine learning categories are important include: De-noising improves user experience by making technology more effective in noisy environments; Anomaly detection helps in preventing crime and securing transactions; Density estimation enhances personalized experiences by understanding probability distributions of data.