AI Training

Unsupervised Learning

In this lesson, we'll cover unsupervised learning, a type of machine learning where the algorithm finds patterns in data without being told what to look for. We'll explore clustering, a popular unsupervised learning technique, and discuss how it's used in real-world applications. We'll also touch on dimensionality reduction, a key step in clustering high-dimensional data.

Why It Matters

Unsupervised learning is crucial in AI because it allows us to understand complex data without labeled examples. This is particularly useful in image and speech recognition, where labeled data is expensive to collect. By using clustering and dimensionality reduction, we can discover hidden patterns in data and make predictions without explicit supervision.

Key Points

• Unsupervised Clustering:: Clustering is an unsupervised learning task that groups similar data points together, revealing hidden patterns in the data.

• Expectation-Maximization (EM) Algorithm: The EM algorithm is a powerful tool for clustering, particularly when dealing with missing or uncertain data.

• K-Means Clustering: K-means is a popular clustering algorithm that assigns each data point to a cluster based on its similarity to the cluster's centroid.

• Dimensionality Reduction:: Dimensionality reduction is a technique that reduces the number of features in high-dimensional data, making it easier to cluster and analyze.

• Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving most of the information.

• Cluster Affinities:: Cluster affinities are measures of how well a data point fits into a cluster, and can be used as features for further processing.

• Feature Engineering:: Feature engineering involves creating new features from existing ones, such as cluster affinities, to improve the performance of machine learning models.

Key Concepts

Expectation-Maximization (EM) Algorithm

A powerful tool for clustering uncertain or missing data

K-Means Clustering

A popular clustering algorithm that assigns data points to clusters based on similarity to the cluster's centroid

Principal Component Analysis (PCA)

A dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving most of the information

From the books

“case; the same problem arises in learning the distributions for the symptoms. This section describes an algorithm called expectation–maximization, or EM, that solves this problem Expectation– maximiza…”

“disentangle the unknown factors of variation underlying the data. In the case of PCA, this disentangling takes the form of ﬁnding a rotation of the input space (described by W) that aligns the princip…”

“unsupervised learning task is clustering: detecting poten- tially useful clusters of input examples. For example, when shown millions of images taken from the Internet, a computer vision system can id…”

Quick Quiz

1. What is the primary goal of unsupervised clustering?

To predict a target variable

To group similar data points together

To classify data into pre-defined categories

2. What is the EM algorithm used for?

Dimensionality reduction

Feature engineering

Clustering uncertain or missing data

3. What is the main purpose of PCA in dimensionality reduction?

To reduce the number of features in high-dimensional data

To increase the number of features in high-dimensional data

To preserve most of the information in high-dimensional data

Fine-Tuning Pretrained Models →