Machine Learning: Clustering & Retrieval from Coursera

Case Studies: Finding Similar Documents

A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover?

In this third case study, finding similar documents, you will examine similarity-based algorithms for retrieval. In this course, you will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce.

Learning Outcomes: By the end of this course, you will be able to:

-Create a document retrieval system using k-nearest neighbors.

-Identify various similarity metrics for text data.

-Reduce computations in k-nearest neighbor search by using KD-trees.

-Produce approximate nearest neighbors using locality sensitive hashing.

-Compare and contrast supervised and unsupervised learning tasks.

-Cluster documents by topic using k-means.

-Describe how to parallelize k-means using MapReduce.

-Examine probabilistic clustering approaches using mixtures models.

-Fit a mixture of Gaussian model using expectation maximization (EM).

-Perform mixed membership modeling using latent Dirichlet allocation (LDA).

-Describe the steps of a Gibbs sampler and how to use its output to draw inferences.

-Compare and contrast initialization techniques for non-convex optimization objectives.

-Implement these techniques in Python.

What's inside

Syllabus

Welcome

Clustering and retrieval are some of the most high-impact machine learning tools out there. Retrieval is used in almost every applications and device we interact with, like in providing a set of products related to one a shopper is currently considering, or a list of people you might want to connect with on a social media platform. Clustering can be used to aid retrieval, but is a more broadly useful tool for automatically discovering structure in data, like uncovering groups of similar patients.

This introduction to the course provides you with an overview of the topics we will cover and the background knowledge and resources we assume you have.

Nearest Neighbor Search

We start the course by considering a retrieval task of fetching a document similar to one someone is currently reading. We cast this problem as one of nearest neighbor search, which is a concept we have seen in the Foundations and Regression courses. However, here, you will take a deep dive into two critical components of the algorithms: the data representation and metric for measuring similarity between pairs of datapoints. You will examine the computational burden of the naive nearest neighbor search algorithm, and instead implement scalable alternatives using KD-trees for handling large datasets and locality sensitive hashing (LSH) for providing approximate nearest neighbors, even in high-dimensional spaces. You will explore all of these ideas on a Wikipedia dataset, comparing and contrasting the impact of the various choices you can make on the nearest neighbor results produced.

Clustering with k-means

In clustering, our goal is to group the datapoints in our dataset into disjoint sets. Motivated by our document analysis case study, you will use clustering to discover thematic groups of articles by "topic". These topics are not provided in this unsupervised learning task; rather, the idea is to output such cluster labels that can be post-facto associated with known topics like "Science", "World News", etc. Even without such post-facto labels, you will examine how the clustering output can provide insights into the relationships between datapoints in the dataset. The first clustering algorithm you will implement is k-means, which is the most widely used clustering algorithm out there. To scale up k-means, you will learn about the general MapReduce framework for parallelizing and distributing computations, and then how the iterates of k-means can utilize this framework. You will show that k-means can provide an interpretable grouping of Wikipedia articles when appropriately tuned.

Mixture Models

In k-means, observations are each hard-assigned to a single cluster, and these assignments are based just on the cluster centers, rather than also incorporating shape information. In our second module on clustering, you will perform probabilistic model-based clustering that provides (1) a more descriptive notion of a "cluster" and (2) accounts for uncertainty in assignments of datapoints to clusters via "soft assignments". You will explore and implement a broadly useful algorithm called expectation maximization (EM) for inferring these soft assignments, as well as the model parameters. To gain intuition, you will first consider a visually appealing image clustering task. You will then cluster Wikipedia articles, handling the high-dimensionality of the tf-idf document representation considered.

Mixed Membership Modeling via Latent Dirichlet Allocation

The clustering model inherently assumes that data divide into disjoint sets, e.g., documents by topic. But, often our data objects are better described via memberships in a collection of sets, e.g., multiple topics. In our fourth module, you will explore latent Dirichlet allocation (LDA) as an example of such a mixed membership model particularly useful in document analysis. You will interpret the output of LDA, and various ways the output can be utilized, like as a set of learned document features. The mixed membership modeling ideas you learn about through LDA for document analysis carry over to many other interesting models and applications, like social network models where people have multiple affiliations.

Throughout this module, we introduce aspects of Bayesian modeling and a Bayesian inference algorithm called Gibbs sampling. You will be able to implement a Gibbs sampler for LDA by the end of the module.

Hierarchical Clustering & Closing Remarks

In the conclusion of the course, we will recap what we have covered. This represents both techniques specific to clustering and retrieval, as well as foundational machine learning concepts that are more broadly useful.

We provide a quick tour into an alternative clustering approach called hierarchical clustering, which you will experiment with on the Wikipedia dataset. Following this exploration, we discuss how clustering-type ideas can be applied in other areas like segmenting time series. We then briefly outline some important clustering and retrieval ideas that we did not cover in this course.

We conclude with an overview of what's in store for you in the rest of the specialization.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides real-world applications and examples of concepts, which is useful for making the course relatable

Uses a project-based approach, allowing learners to get hands-on experience with the topics being covered

Emphasizes the application of concepts in real-world scenarios, making it relevant to various fields and industries

Helps learners gain a solid understanding of key concepts and methodologies in data analysis

Requires learners to have some prior understanding of data analysis fundamentals

Incorporates a mix of lectures and interactive exercises to make the learning process more engaging and effective

Reviews summary

Helpful and informative ml course

Learners say that "Machine Learning: Clustering & Retrieval" is a largely positive course that provides a deep dive into machine learning concepts. The course is well-received for its engaging assignments, detailed explanations, and helpful coding examples. Reviewers especially appreciate the instructors' expertise and the course's focus on practical applications. Overall, learners recommend this course to anyone interested in gaining a strong foundation in machine learning.

The course's emphasis on practical applications is appreciated by learners.

"I liked that the course focused on practical applications of machine learning."

"The course provides a good foundation for applying machine learning to real-world problems."

"I feel confident that I can apply the skills I learned in this course to my own projects."

Learners highly value the expertise of the course instructors.

"The instructors are very knowledgeable and passionate about the subject matter."

"I really enjoyed the instructors' teaching style."

"The instructors were very responsive to questions and provided helpful feedback."

The course's focus on practical examples is another key factor in its positive reception.

"The coding examples were especially helpful in understanding how to apply the concepts to real-world problems."

"I found the hands-on exercises to be very helpful in reinforcing the concepts."

"The course provides a lot of practical examples that help to illustrate the concepts."

Learners praise the course's assignments for being both challenging and rewarding.

"I loved the assignments. They were challenging but also very helpful in reinforcing the concepts we learned in the lectures."

"The programming assignments were really well-designed and helped me to apply the concepts I was learning to real-world problems."

"I especially enjoyed the final project, which allowed me to put everything I had learned in the course to use."

Reviewers consistently mention the clarity of the course's explanations.

"The instructors did a great job of explaining the concepts in a way that was easy to understand."

"I really appreciated the detailed explanations and examples provided in the lecture videos."

"The course materials were very well-organized and easy to follow."

A few learners find some of the concepts challenging.

"While the topics covered in this course are arguably more complex than those in other courses in the Machine Learning specialization, I felt that the instructor did not do a good job covering the complicated material."

"I had to use a ton of outside resources to augment the videos presented as part of this course."

"Many of the statistical terms and concepts were unfamiliar to me."

Some learners express frustration with outdated assignments.

"Many of the assignments have errors and bugs in the code that have not been updated."

"The course is pretty much abandoned and outdated."

"The assignments in the end and worked out examples were what turned out to be helpful at the end of the day, so kudos for providing them. I overall liked the journey and hopefully looking forward to implement the skills I have imbibed. Thank you and stay safe!"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Machine Learning: Clustering & Retrieval with these activities:

Document Retrieval Discussion Group

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Foster collaboration and knowledge sharing on different document retrieval approaches.

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Join a group of peers to discuss the case studies presented in the course.
Share insights on the challenges and potential solutions for different document retrieval scenarios.
Collaborate on developing a case study of your own.

Nearest Neighbor Code Challenges

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Help solidify foundational knowledge of nearest neighbor search algorithms.

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Implement a basic k-nearest neighbors algorithm from scratch.
Explore different distance metrics, such as Euclidean distance and cosine similarity.
Optimize the k-nearest neighbors algorithm using KD-trees.

Advanced Topic Modeling Tutorials

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Help bridge the gap between course material and cutting-edge research in topic modeling.

Browse courses on Topic Modeling

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Explore tutorials on advanced topic modeling techniques, such as hierarchical LDA.
Implement these techniques in a programming language.
Apply the techniques to analyze a large corpus of text.

Five other activities

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Show all eight activities

Pattern Recognition and Machine Learning

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Provide additional context and depth on clustering and retrieval algorithms.

View Pattern Recognition and Machine Learning... on Amazon

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Read Chapter 10: Clustering and Dimensionality Reduction.
Focus on sections covering k-means and latent Dirichlet allocation (LDA).

Machine Learning for Text Analysis Workshop

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Provide a deeper dive into the application of machine learning techniques for text analysis.

Browse courses on Machine Learning

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Attend a workshop that covers advanced clustering algorithms.
Learn about different text preprocessing techniques.
Engage in hands-on exercises to apply these techniques to real-world datasets.

Visualize Document Similarity

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Foster deeper understanding of document similarity and clustering through visualization.

Browse courses on Data Visualization

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Create a scatter plot of documents, using their TF-IDF feature vectors as coordinates.
Highlight documents that belong to the same cluster.
Use color or shape to represent different topics identified through LDA.

Contribute to a Text Mining Library

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Provide hands-on experience with practical applications of text mining techniques.

Browse courses on Open Source

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Identify an open-source text mining library.
Contribute a new feature or improvement to the library.
Engage with the library's community and contribute to discussions.

Text Summarization Tool

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Allow students to apply course concepts to a practical problem and build a valuable tool.

Browse courses on Natural Language Processing

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Use topic modeling techniques to identify key topics in a document.
Design and implement a system to extract and merge relevant sentences related to those topics.
Validate the tool's performance on a dataset of documents.

Career center

Learners who complete Machine Learning: Clustering & Retrieval will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers design and develop machine learning models. They work on a variety of tasks, including data preprocessing, model training, and model deployment. The Machine Learning: Clustering & Retrieval course provides a strong foundation for a career as a Machine Learning Engineer. The course covers topics such as nearest neighbor search, clustering, mixture models, and mixed membership modeling. These topics are essential for understanding how to design and develop machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts mine data to extract actionable insights. They analyze data using a variety of techniques, including statistics, machine learning, and data visualization. The Machine Learning: Clustering & Retrieval course provides a strong foundation for a career as a Data Analyst. The course covers topics such as nearest neighbor search, clustering, mixture models, and mixed membership modeling. These topics are essential for understanding how to analyze data and extract meaningful insights.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists use data to solve business problems. They work on a variety of tasks, including data analysis, data visualization, and machine learning. The Machine Learning: Clustering & Retrieval course provides a strong foundation for a career as a Data Scientist. The course covers topics such as nearest neighbor search, clustering, mixture models, and mixed membership modeling. These topics are essential for understanding how to use data to solve business problems.

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Statistician

Statisticians collect, analyze, and interpret data. They work on a variety of projects, including public health, finance, and marketing. The Machine Learning: Clustering & Retrieval course provides a strong foundation for a career as a Statistician. The course covers topics such as nearest neighbor search, clustering, mixture models, and mixed membership modeling. These topics are essential for understanding how to collect, analyze, and interpret data.

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Software Engineer

Software Engineers design, develop, and maintain software applications. They work on a variety of projects, including web applications, mobile applications, and desktop applications. The Machine Learning: Clustering & Retrieval course provides a strong foundation for a career as a Software Engineer. The course covers topics such as nearest neighbor search, clustering, mixture models, and mixed membership modeling. These topics are essential for understanding how to design and develop software applications.

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Product Manager

Product Managers develop and manage software products. They work on a variety of tasks, including market research, product design, and user experience. The Machine Learning: Clustering & Retrieval course provides a strong foundation for a career as a Product Manager. The course covers topics such as nearest neighbor search, clustering, mixture models, and mixed membership modeling. These topics are essential for understanding how to develop and manage software products.

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Consultant

Consultants provide advice to businesses and organizations. They work on a variety of projects, including strategy development, organizational change, and risk management. The Machine Learning: Clustering & Retrieval course provides a strong foundation for a career as a Consultant. The course covers topics such as nearest neighbor search, clustering, mixture models, and mixed membership modeling. These topics are essential for understanding how to provide advice to businesses and organizations.

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Data Engineer

Data Engineers design and build data pipelines. They work on a variety of projects, including data collection, data storage, and data processing. The Machine Learning: Clustering & Retrieval course provides a strong foundation for a career as a Data Engineer. The course covers topics such as nearest neighbor search, clustering, mixture models, and mixed membership modeling. These topics are essential for understanding how to design and build data pipelines.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. They work on a variety of tasks, including risk management, portfolio optimization, and trading. The Machine Learning: Clustering & Retrieval course provides a strong foundation for a career as a Quantitative Analyst. The course covers topics such as nearest neighbor search, clustering, mixture models, and mixed membership modeling. These topics are essential for understanding how to analyze financial data and make informed decisions.

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Researcher

Researchers conduct research to advance knowledge. They work on a variety of topics, including science, technology, and social sciences. The Machine Learning: Clustering & Retrieval course provides a strong foundation for a career as a Researcher. The course covers topics such as nearest neighbor search, clustering, mixture models, and mixed membership modeling. These topics are essential for understanding how to conduct research and advance knowledge.

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Information Architect

Information Architects design and organize websites and other digital products. They work on a variety of projects, including user experience design, content strategy, and search engine optimization. The Machine Learning: Clustering & Retrieval course provides a strong foundation for a career as an Information Architect. The course covers topics such as nearest neighbor search, clustering, mixture models, and mixed membership modeling. These topics are essential for understanding how to design and organize websites and other digital products.

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Project Manager

Project Managers plan and execute projects. They work on a variety of projects, including software development, construction, and event management. The Machine Learning: Clustering & Retrieval course provides a strong foundation for a career as a Project Manager. The course covers topics such as nearest neighbor search, clustering, mixture models, and mixed membership modeling. These topics are essential for understanding how to plan and execute projects.

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Academic

Academics teach and conduct research at universities and colleges. They work on a variety of topics, including science, technology, and social sciences. The Machine Learning: Clustering & Retrieval course provides a strong foundation for a career as an Academic. The course covers topics such as nearest neighbor search, clustering, mixture models, and mixed membership modeling. These topics are essential for understanding how to teach and conduct research at universities and colleges.

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Business Analyst

Business Analysts identify and solve business problems. They work on a variety of projects, including process improvement, system design, and data analysis. The Machine Learning: Clustering & Retrieval course provides a strong foundation for a career as a Business Analyst. The course covers topics such as nearest neighbor search, clustering, mixture models, and mixed membership modeling. These topics are essential for understanding how to identify and solve business problems.

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Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve business problems. They work on a variety of projects, including supply chain management, logistics, and healthcare. The Machine Learning: Clustering & Retrieval course provides a strong foundation for a career as an Operations Research Analyst. The course covers topics such as nearest neighbor search, clustering, mixture models, and mixed membership modeling. These topics are essential for understanding how to use mathematical and statistical models to solve business problems.

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