Other

Unsupervised Learning: Techniques, Algorithms, and Applications

0 purchase

Course
COMPUTER SCIENCE

Institution
Harvard University

This document explores unsupervised learning, focusing on its key techniques, algorithms, and applications. It covers clustering methods like K-means and hierarchical clustering, as well as dimensionality reduction techniques such as Principal Component Analysis (PCA). The document also highlights ...

[Show more]

Preview 2 out of 6 pages

View example

Uploaded on January 31, 2025
Number of pages 6
Written in 2024/2025
Type Other
Person Unknown

machine learning
cs1004
unsupervised learning
clustering
dimensionality reduction
k means
hierarchical clustering
gaussian mixture models
anomaly detection
data prepro
principal component analysis pca

$4.69

Also available in package deal from $56.79

Add to cart

Save

100% satisfaction guarantee
Immediately available after payment
Both online and in PDF
No strings attached

Also available in package deal (1)

Machine Learning & AI Complete Exam Study Pack (24 Documents)

$ 135.96 $ 56.79 24 items

1. Other - Foundations of artificial intelligence: key concepts and applications
2. Other - Types of artificial intelligence (ai): narrow, general, and superintelligent ai
3. Other - Introduction to machine learning: concepts, techniques, and applications
4. Other - The machine learning process: from data collection to model evaluation
5. Other - Machine learning algorithms: key models and techniques
6. Other - Applications of artificial intelligence: transforming industries and everyday life
7. Other - Deep learning: concepts and techniques
8. Other - Challenges in machine learning and artificial intelligence: overcoming barriers to pr...
9. Other - Key concepts in machine learning: foundations and techniques
10. Other - Supervised learning: concepts, algorithms, and applications
11. Other - Unsupervised learning: techniques, algorithms, and applications
12. Other - Reinforcement learning: concepts, algorithms, and applications
13. Other - Evaluation metrics in machine learning: measuring model performance
14. Other - Natural language processing (nlp): concepts, techniques, and applications
15. Other - Computer vision: concepts, techniques, and applications
16. Other - Ai and big data: concepts, technologies, and applications
17. Other - Ai in cloud computing: integration, benefits, and applications
18. Other - Ai and ethics: challenges, principles, and implications
19. Other - The future of ai and machine learning: trends, innovations, and challenges
20. Other - Real-world use cases of ai and machine learning
21. Other - Full guide on machine learning: concepts, algorithms, and applications
22. Other - Full guide on artificial intelligence: concepts, technologies, and future trends
23. Other - Question and answers on machine learning
24. Other - Question and answers on artificial intelligence
Show more

Unsupervised Learning
Unsupervised learning is a type of machine learning where the algorithm is
provided with data that is not labeled. Unlike supervised learning, where the
algorithm learns from input-output pairs, unsupervised learning aims to find
hidden patterns, structures, or relationships in the data without prior knowledge
of the output. This approach is particularly useful when you don’t have labeled
data but want to extract meaningful insights or organize the data in some way.

What is Unsupervised Learning?
In unsupervised learning, the algorithm is tasked with identifying hidden patterns
or structures within a set of data. The primary goal is to explore the data and
learn its inherent structure, relationships, or distributions, without the guidance
of labeled examples.

 Unlabeled Data: The key feature of unsupervised learning is that the data
used for training does not have predefined labels or categories. Instead, the
algorithm tries to group, segment, or organize the data based on
similarities or common features.
 Exploratory Nature: Since the output labels are not provided, unsupervised
learning is often used in exploratory data analysis, anomaly detection, and
clustering tasks.

Types of Unsupervised Learning Tasks
Unsupervised learning tasks can be divided into two primary categories:

1. Clustering Clustering is the task of grouping similar data points together
into clusters or groups. The goal is to find natural groupings in the data
based on similarity.
o How It Works: The algorithm identifies patterns in the data and
groups similar data points into clusters. Data points within the same
cluster share common characteristics, and the algorithm strives to

, minimize the distance or dissimilarity between points in the same
cluster.
o Applications: Clustering is widely used in customer segmentation,
image compression, and grouping documents or text data based on
topics.
o Example: In a marketing campaign, clustering can be used to
segment customers based on purchasing behavior to create targeted
marketing strategies.
2. Dimensionality Reduction Dimensionality reduction aims to reduce the
number of features or variables in a dataset while retaining as much
information as possible. This process simplifies the dataset and can help
improve the performance of machine learning algorithms.
o How It Works: Dimensionality reduction techniques try to capture
the most important aspects of the data while discarding less
important or redundant features.
o Applications: Dimensionality reduction is often used in areas like
image processing (e.g., reducing the number of pixels in an image),
feature extraction, and data visualization.
o Example: Reducing the number of features in a dataset of customer
information while preserving patterns that distinguish different
customer segments.

The Unsupervised Learning Process
While supervised learning involves labeled data, unsupervised learning focuses on
discovering hidden patterns in unlabeled data. The general process for
unsupervised learning is as follows:

1. Data Collection: Just like in supervised learning, the first step is gathering a
dataset. However, the data in unsupervised learning does not include any
labels or target values.
2. Data Preprocessing: Before applying unsupervised learning algorithms, the
data must be cleaned and prepared. This step may involve normalizing or
scaling the data, handling missing values, and removing outliers.
3. Model Selection: Once the data is ready, the next step is to choose an
unsupervised learning algorithm. Common algorithms for clustering include

The benefits of buying summaries with Stuvia:

Guaranteed quality through customer reviews

Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.

Quick and easy check-out

You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.

Focus on what matters

Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!

Frequently asked questions

What do I get when I buy this document?

You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.

Satisfaction guarantee: how does it work?

Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.

Who am I buying these notes from?

Stuvia is a marketplace, so you are not buying this document from us, but from seller rileyclover179. Stuvia facilitates payment to the seller.

Will I be stuck with a subscription?

No, you only buy these notes for $4.69. You're not tied to anything after your purchase.

Can Stuvia be trusted?

4.6 stars on Google & Trustpilot (+1000 reviews)

65004 documents were sold in the last 30 days

Founded in 2010, the go-to place to buy study notes for 15 years now

Start selling

Seller

Class notes ·

Case ·

Exam (elaborations) ·

Other

Unsupervised Learning: Techniques, Algorithms, and Applications

Document information

Subjects

Written for