Introduction to Machine Learning: Unsupervised Learning

University of Colorado Boulder via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Paid Certificate Available
Duration & workload

14 hours 54 minutes
Sessions

On-Demand
Subtitles

English, Arabic, French, Portuguese, German, Thai, Indonesian, Spanish, Kazakh, Hindi, Russian, Swedish, Korean, Greek, Italian, Chinese, Ukrainian, Japanese, Polish, Dutch, Turkish

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Overview

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Introduction to Machine Learning: Unsupervised Learning explores how machines uncover structure, patterns, and relationships in data without labeled outcomes. In this course, you’ll learn how to analyze and visualize high-dimensional data using Principal Component Analysis, discover natural groupings through clustering methods like K-Means and hierarchical clustering, and tackle real-world challenges such as missing data and recommender systems. Through hands-on practice and thoughtful interpretation, you’ll build the intuition and practical skills needed to extract insight from complex, unlabeled datasets. This course can be taken for academic credit as part of CU Boulder’s Masters of Science in Computer Science (MS-CS), Master of Science in Artificial Intelligence (MS-AI), and Master of Science in Data Science (MS-DS) degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Artificial Intelligence: https://www.coursera.org/degrees/ms-artificial-intelligence-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder

Syllabus

Unsupervised Learning Basics & Exploratory Data Analysis

Welcome to Introduction to Machine Learning: Unsupervised Learning. In this first module, you will explore how machine learning can uncover hidden patterns in data, without relying on labeled outcomes. You will learn how unsupervised learning differs from supervised learning, and why the absence of a “correct answer” makes interpretation both powerful and challenging. Through Principal Component Analysis (PCA), you will discover how to reduce the dimensionality of complex datasets while preserving the most important variation. You will compute principal components, interpret explained variance, and visualize high-dimensional data in two dimensions. By the end of this module, you will have a hands-on understanding of how PCA can reveal structure in seemingly chaotic data.

Principal Component Analysis (PCA)

Now that you understand the basics of Principal Component Analysis, this module focuses on how to apply it thoughtfully. You will learn how to decide how many components to retain by examining the proportion of variance explained and interpreting scree plots. You will also explore how to interpret principal component loadings to understand what each component reveals about the original features. Through hands-on practice, you will see how PCA can be used not only for visualization but also as a powerful pre-processing step before supervised learning. By the end of this module, you will be able to reduce dimensionality with purpose and insight.

K-Means Clustering

This module introduces you to the world of clustering, where the goal is to uncover natural groupings in data without any labels. You will learn how the k-means algorithm partitions observations into clusters based on similarity, and how it iteratively refines those groupings by updating centroids. Along the way, you will grapple with the challenge of choosing the right number of clusters and explore heuristic tools like the elbow method. Through hands-on work, you will evaluate clustering results and interpret what each group represents in context. Clustering is as much an art as it is a science, and this module will help you build intuition for both.

Hierarchical Clustering

In this module, you will explore hierarchical clustering—a method that builds nested groupings without requiring you to choose the number of clusters in advance. You will learn how the agglomerative approach works step by step and how to interpret dendrograms to uncover meaningful structure in your data. Unlike K-means, hierarchical clustering offers a full picture of how observations relate to one another at different levels of similarity. You will also examine how scaling and distance metrics can influence clustering results, and why evaluating clusters is often more subjective than definitive. This module encourages you to think critically about what makes a clustering solution useful, not just mathematically valid.

Matrix Completion, Missing Values, and Recommender Systems

This module introduces low-rank matrix completion as a principled approach to handling missing data and powering recommender systems. You will learn how PCA can be used as a matrix approximation tool to reconstruct missing entries, implement an iterative completion algorithm, and validate model choices via masking. A compact case study demonstrates practical trade-offs with small p, and the module concludes by mapping the same ideas to user–item recommendation with attention to preprocessing, evaluation, scale, and ethics.