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Michael D. Ekstrand
and
Joseph A Konstan
In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.
Register for this course and see more details by visiting:
OpenCourser.com/course/lxp8ew/matrix
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What's inside
Syllabus
Preface
Matrix Factorization (Part 1)
This is a two-part, two-week module on matrix factorization recommender techniques. It includes an assignment and quiz (both due in the second week), and an honors assignment (also due in the second week). Please pace yourself carefully -- it will be difficult to finish in two weeks unless you start the assignments during the first week.
Read more
Syllabus
Preface
Matrix Factorization (Part 1)
This is a two-part, two-week module on matrix factorization recommender techniques. It includes an assignment and quiz (both due in the second week), and an honors assignment (also due in the second week). Please pace yourself carefully -- it will be difficult to finish in two weeks unless you start the assignments during the first week.
Read more
Matrix Factorization (Part 2)
Hybrid Recommenders
This is a three-part, two-week module on hybrid and machine learning recommendaton algorithms and advanced recommender techniques. It includes a quiz (due in the second week), and an honors assignment (also due in the second week). Please pace yourself carefully -- it will be difficult to finish the honors track in two weeks unless you start the assignments during the first week.
Advanced Machine Learning
Advanced Topics
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops matrix factorization and hybrid machine learning skills for recommender systems, which are core skills for data analysts and software engineers working with user behavior data
Taught by Joseph A Konstan and Michael D. Ekstrand, who are recognized for their work in recommender systems
Covers advanced topics in recommender systems, such as advanced machine learning and advanced matrix factorization, which are highly relevant in industry
Provides both intuition and practical details of building recommender systems, which is useful for learners looking to implement these systems in the real world
Requires that learners come in with extensive background knowledge first, which may be a barrier for some learners
Save this course
Save Matrix Factorization and Advanced Techniques to your list so you can find it easily later:
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Reviews summary
Advanced matrix factorization
According to students, learners say this advanced course in matrix factorization delivers a great amount of material that is well presented. The guidelines for developing, employing, and evaluation of a recommendation system are valuable.
The course is well presented with lots of depth.
"Based on my experience with the previous courses in this specialization, I was very positively surprised by the amount and depth of material provided in this course."
"It covers almost everything that is there to be known."
The course provides guidelines that are beneficial.
"Also, this course provides guidelines as to how to develop, employ and evaluate a recommender system in real life."
"It covers almost everything that is there to be known."
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 Matrix Factorization and Advanced Techniques with these
activities:
Solve matrix factorization decomposition problems
Show steps
Solving problems will help you understand the concepts and algorithms of matrix factorization in the context of recommender systems.
Browse courses on
Matrix Factorization
Show steps
-
Review the lecture notes and textbook materials on matrix factorization.
-
Attempt to solve the practice problems at the end of each chapter.
-
If you encounter any difficulties, seek help from the course instructor or teaching assistant.
Follow tutorials on hybrid recommender systems
Show steps
Following tutorials will provide you with hands-on experience in building and evaluating hybrid recommender systems, which are a key component of this course.
Browse courses on
Hybrid Recommender Systems
Show steps
-
Identify reputable online tutorials or resources that cover hybrid recommender systems.
-
Follow the tutorials step-by-step and implement the algorithms discussed.
-
Experiment with different parameters and data sets to observe the impact on recommendation accuracy.
Write a blog post on advanced recommender techniques
Show steps
Writing a blog post will help you synthesize and communicate your understanding of advanced recommender techniques covered in the course.
Browse courses on
Recommender Systems
Show steps
-
Choose a specific topic related to advanced recommender techniques.
-
Research the topic thoroughly and gather relevant information from credible sources.
-
Organize your thoughts and outline the structure of your blog post.
-
Write a clear and engaging blog post that explains the topic in a structured and understandable way.
-
Proofread your blog post carefully before sharing it with others.
Two other activities
Expand to see all activities and additional details
Show all five activities
Participate in a Kaggle competition on recommender systems
Show steps
Participating in a Kaggle competition will provide you with a challenging and practical way to apply your knowledge of recommender systems and compete with other data scientists.
Browse courses on
Recommender Systems
Show steps
-
Identify a relevant Kaggle competition focused on recommender systems.
-
Carefully read the competition guidelines and familiarize yourself with the data and evaluation metrics.
-
Develop and implement a recommender system using the techniques covered in the course.
-
Submit your solution and track your progress on the competition leaderboard.
-
Analyze the results of your submission and identify areas for improvement.
Contribute to an open-source recommender system project
Show steps
Contributing to an open-source project will allow you to gain practical experience in developing and maintaining recommender systems.
Browse courses on
Recommender Systems
Show steps
-
Identify a reputable open-source recommender system project that aligns with your interests.
-
Familiarize yourself with the project's codebase, documentation, and community guidelines.
-
Identify an area where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
-
Make your proposed changes or contributions to the project.
-
Submit a pull request and engage with the project maintainers to get your changes reviewed and merged.
Solve matrix factorization decomposition problems
Show steps
Solving problems will help you understand the concepts and algorithms of matrix factorization in the context of recommender systems.
Browse courses on
Matrix Factorization
Show steps
Show steps
Show steps
- Review the lecture notes and textbook materials on matrix factorization.
- Attempt to solve the practice problems at the end of each chapter.
- If you encounter any difficulties, seek help from the course instructor or teaching assistant.
Follow tutorials on hybrid recommender systems
Show steps
Following tutorials will provide you with hands-on experience in building and evaluating hybrid recommender systems, which are a key component of this course.
Browse courses on
Hybrid Recommender Systems
Show steps
Show steps
Show steps
- Identify reputable online tutorials or resources that cover hybrid recommender systems.
- Follow the tutorials step-by-step and implement the algorithms discussed.
- Experiment with different parameters and data sets to observe the impact on recommendation accuracy.
Write a blog post on advanced recommender techniques
Show steps
Writing a blog post will help you synthesize and communicate your understanding of advanced recommender techniques covered in the course.
Browse courses on
Recommender Systems
Show steps
Show steps
Show steps
- Choose a specific topic related to advanced recommender techniques.
- Research the topic thoroughly and gather relevant information from credible sources.
- Organize your thoughts and outline the structure of your blog post.
- Write a clear and engaging blog post that explains the topic in a structured and understandable way.
- Proofread your blog post carefully before sharing it with others.
Two other activities
Expand to see all activities and additional details
Show all five activities
Participate in a Kaggle competition on recommender systems
Show steps
Participating in a Kaggle competition will provide you with a challenging and practical way to apply your knowledge of recommender systems and compete with other data scientists.
Browse courses on
Recommender Systems
Show steps
Show steps
Show steps
- Identify a relevant Kaggle competition focused on recommender systems.
- Carefully read the competition guidelines and familiarize yourself with the data and evaluation metrics.
- Develop and implement a recommender system using the techniques covered in the course.
- Submit your solution and track your progress on the competition leaderboard.
- Analyze the results of your submission and identify areas for improvement.
Contribute to an open-source recommender system project
Show steps
Contributing to an open-source project will allow you to gain practical experience in developing and maintaining recommender systems.
Browse courses on
Recommender Systems
Show steps
Show steps
Show steps
- Identify a reputable open-source recommender system project that aligns with your interests.
- Familiarize yourself with the project's codebase, documentation, and community guidelines.
- Identify an area where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
- Make your proposed changes or contributions to the project.
- Submit a pull request and engage with the project maintainers to get your changes reviewed and merged.
Career center
Learners who complete Matrix Factorization and Advanced Techniques will develop knowledge and skills
that may be useful to these careers:
Computational Scientist
Computational Scientist
Computational Scientists use mathematical and computational techniques to solve complex problems in science and engineering. This course can help Computational Scientists develop the skills needed to use computational techniques to solve complex problems.
Market Researcher
Market Researcher
Market Researchers conduct research to understand consumer behavior and market trends. This course can help Market Researchers develop the skills needed to conduct research and analyze data to understand consumer behavior and market trends.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex problems in business and industry. This course can help Operations Research Analysts build a foundation in valuable mathematical and analytical techniques.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. They use statistical methods to develop models that can be used to make predictions and decisions, a skill that can be applied to nearly any industry. This course can help Statisticians build a foundation in valuable statistical methods.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. This course may be useful for Actuaries because it can help them develop the skills needed to build and analyze models that can assess risk and uncertainty.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment recommendations. This course may be useful for Quantitative Analysts because it can help them develop the skills needed to build and analyze financial models.
Software Developer
Software Developer
Software Developers design, develop, and maintain software applications. This course may be useful for Software Developers because it can help them develop the skills needed to design and develop software applications that are efficient and effective.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. This course may be useful for Machine Learning Engineers because it can help them develop the skills needed to build and deploy machine learning models that can make accurate predictions.
Database Administrator
Database Administrator
Database Administrators design, implement, and maintain databases. This course is a great way to develop the skills needed to design and implement databases that are efficient and effective.
Data Scientist
Data Scientist
Data Scientists leverage their knowledge of statistics, programming, and machine learning to extract meaningful insights from data. This course can help Data Scientists build a foundation that can be useful in their roles. For ambitious Data Scientists, this course has an honors track with more challenging assignments.
Product Manager
Product Manager
Product Managers are responsible for the development and management of products. This course may be useful for Product Managers because it can help them develop the skills needed to develop and manage products that meet the needs of customers.
Data Engineer
Data Engineer
Data Engineers design, construct, and manage an organization's data architecture. They also build and maintain the infrastructure that stores and processes data. This course may be useful for Data Engineers because it can help them develop the skills needed to design and build data infrastructures that efficiently store and process large amounts of data.
Business Analyst
Business Analyst
Business Analysts analyze business processes and systems to identify areas for improvement. This course may be useful for Business Analysts because it can help them develop the skills needed to analyze business processes and systems to identify areas for improvement.
Data Analyst
Data Analyst
Data Analysts apply specialized computer programming skills to analyze large sets of data to identify trends and patterns. While this role doesn't necessarily require an advanced degree, a course in Matrix Factorization and Advanced Techniques may be useful. This course is a great way to develop the specialized computer programming skills that are so important in this role.
Information Systems Manager
Information Systems Manager
Information Systems Managers plan, implement, and manage information systems. This course may be useful for Informatino Systems Managers because it can help them develop the skills needed to plan, implement, and manage information systems that are efficient and effective.
For more career information including salaries, visit:
OpenCourser.com/course/lxp8ew/matrix
Reading list
We've selected 15 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Matrix Factorization and Advanced Techniques.
Provides a comprehensive overview of recommender systems, covering a wide range of topics, including matrix factorization, hybrid recommenders, and advanced machine learning techniques.
This handbook provides a comprehensive overview of recommender systems, covering a wide range of topics, including matrix factorization, hybrid recommenders, and advanced machine learning techniques.
Provides a comprehensive overview of recommender systems. It valuable resource for anyone who wants to learn more about this field.
Provides a comprehensive overview of recommender systems algorithms and evaluation methods. It covers various techniques, including collaborative filtering, content-based filtering, and hybrid approaches.
Provides a comprehensive overview of matrix factorization and SVD. It valuable resource for anyone who wants to learn more about these topics.
Provides a practical introduction to machine learning using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers a wide range of machine learning algorithms and techniques, with a focus on hands-on examples and code snippets.
Provides a gentle introduction to machine learning using Python. It covers the basics of supervised and unsupervised learning, as well as more advanced topics such as neural networks and deep learning. This book great starting point for those new to machine learning.
Provides a comprehensive overview of information retrieval. It valuable resource for anyone who wants to learn more about this field.
Save
Provides a comprehensive overview of data mining. It valuable resource for anyone who wants to learn more about this field.
Provides a comprehensive overview of numerical linear algebra. It valuable resource for anyone who wants to learn more about this topic.
Provides a comprehensive overview of linear algebra and matrix analysis, with a focus on applications in statistics. It covers topics such as matrix theory, vector spaces, and eigenvalues and eigenvectors.
Save
Provides a comprehensive overview of machine learning. It valuable resource for anyone who wants to learn more about this field.
Provides a comprehensive overview of reinforcement learning. It valuable resource for anyone who wants to learn more about this field.
Provides a comprehensive overview of convex optimization. It valuable resource for anyone who wants to learn more about this field.
Save
Provides a comprehensive overview of deep learning. It valuable resource for anyone who wants to learn more about this field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/lxp8ew/matrix
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Machine Learning Capstone
OpenCourser.com/course/i7apkf/machine
Machine Learning Capstone
Via
Coursera
Institution
University of Minnesota
Instructors
Michael D. Ekstrand
Joseph A Konstan
Language
English
Transcripts
Español
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Deutsch
Italiano
中文
العربية
한국어
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پښتو
آذربایجان دیلی
This course belongs to a
collection
called
Recommender Systems. It's comprised of five courses:
- Introduction to Recommender Systems: Non-Personalized and Content-Based
- Nearest Neighbor Collaborative Filtering
- Recommender Systems Capstone
- Matrix Factorization and Advanced Techniques (viewing)
- Recommender Systems: Evaluation and Metrics
Good to know
Develops matrix factorization and hybrid machine learning skills for recommender systems, which are core skills for data analysts and software engineers working with user behavior data
Taught by Joseph A Konstan and Michael D. Ekstrand, who are recognized for their work in recommender systems
Covers advanced topics in recommender systems, such as advanced machine learning and advanced matrix factorization, which are highly relevant in industry
Provides both intuition and practical details of building recommender systems, which is useful for learners looking to implement these systems in the real world
Requires that learners come in with extensive background knowledge first, which may be a barrier for some learners
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