The research team's goal is to discover inherent structures, such as natural groupings and anomalies, within a dataset that lacks predefined labels. This is the primary objective of unsupervised learning. This machine learning paradigm is specifically designed to analyze unlabeled data to identify hidden patterns or intrinsic structures. Techniques like clustering would be used to find the "natural groupings of machine behavior," and anomaly detection would identify "potential anomalies, malfunctions." The problem description directly aligns with the definition and application of unsupervised learning.

A. Reinforcement learning involves an agent learning to make decisions by interacting with an environment to maximize a reward signal, which is not the scenario described.

C. Deep learning is a subfield of machine learning that uses multi-layered neural networks; it is a set of techniques, not a fundamental learning type defined by data labeling.

D. Supervised learning requires a dataset with labeled outcomes (e.g., data points explicitly marked as 'normal' or 'faulty') to train a model, which the team does not have.

1. Google Cloud Documentation: In "What is machine learning?"

Google Cloud defines the three types of machine learning. It states

"Unsupervised learning helps you find structure in your data where you don't have any labels... A common use of unsupervised learning is to cluster data into groups with similar characteristics." This directly supports using unsupervised learning for finding "natural groupings" in the unlabeled sensor data.

Source: Google Cloud

"AI & Machine Learning Products

" What is machine learning? section.

2. Stanford University Courseware (CS229): The course notes for CS229 Machine Learning define unsupervised learning as the task where "we are given data that does not have any labels associated with it." It lists clustering as a canonical example

where the goal is to group the data into coherent "clusters." This aligns perfectly with the team's objective.

Source: Ng

A. (2023). CS229 Machine Learning Course Notes

Stanford University

Part I: Supervised Learning

Section 1: Introduction

page 3.

3. MIT OpenCourseWare: In the "Introduction to Machine Learning" course

unsupervised learning is described as the process of learning from "raw

unlabeled data." The goal is to "discover the underlying structure or distribution of the data

" including tasks like clustering and dimensionality reduction

which are relevant to the described problem.

Source: MIT OpenCourseWare

6.036 Introduction to Machine Learning

Fall 2020

Lecture 1: Introduction

Section on "Paradigms of Machine Learning."