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Michael D. Ekstrand
and
Joseph A Konstan
This capstone project course for the Recommender Systems Specialization brings together everything you've learned about recommender systems algorithms and evaluation into a comprehensive recommender analysis and design project. You will be given a case study to complete where you have to select and justify the design of a recommender system through analysis of recommender goals and algorithm performance.
Read more
This capstone project course for the Recommender Systems Specialization brings together everything you've learned about recommender systems algorithms and evaluation into a comprehensive recommender analysis and design project. You will be given a case study to complete where you have to select and justify the design of a recommender system through analysis of recommender goals and algorithm performance.
Read more
This capstone project course for the Recommender Systems Specialization brings together everything you've learned about recommender systems algorithms and evaluation into a comprehensive recommender analysis and design project. You will be given a case study to complete where you have to select and justify the design of a recommender system through analysis of recommender goals and algorithm performance.
Learners in the honors track will focus on experimental evaluation of the algorithms against medium sized datasets. The standard track will include a mix of provided results and spreadsheet exploration.
Both groups will produce a capstone report documenting the analysis, the selected solution, and the justification for that solution.
Register for this course and see more details by visiting:
OpenCourser.com/course/6ilrhk/recommender
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What's inside
Syllabus
Capstone Project
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for individuals with prior experience in recommender systems or a related field seeking to enhance their understanding and skills
Taught by instructors with extensive knowledge and experience in recommender systems research and industry applications
Provides a comprehensive overview of recommender systems algorithms, evaluation techniques, and practical considerations
Develops critical thinking and analytical skills through a project-based approach involving analysis and design of recommender systems
Offers two tracks (standard and honors) to cater to varying levels of learner experience and interest
Save this course
Save Recommender Systems Capstone to your list so you can find it easily later:
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Reviews summary
Challenging capstone course
Learners say the workload is difficult and unreasonable. They say to start early.
Workload is excessive.
"The workload was quite reasonable in the first four courses of the recommenders specialization, but this one was a RIDICULOUS amount of work to be done in 10 days."
"You research, experiment, and write a 10 page report in the first 7 days."
Course timeline is short.
"If you decide to take this course, make sure to start at least a week early!"
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 Recommender Systems Capstone with these
activities:
Refresh your statistics skills
Show steps
Review the basics of statistics to strengthen your understanding of the mathematical foundations of recommender systems.
Browse courses on
Statistical Analysis
Show steps
-
Review your lecture notes or textbooks
-
Complete practice problems
-
Attend a refresher course or workshop
-
Join a study group or online forum for support
Refresh your knowledge of machine learning
Show steps
Review the fundamentals of machine learning to build upon your prior knowledge and enhance your understanding of recommender systems.
Browse courses on
Machine Learning Algorithms
Show steps
-
Review your lecture notes or textbooks
-
Complete practice problems
-
Attend a refresher course or workshop
-
Join a study group or online forum for support
Explore online recommender systems tutorials
Show steps
Review the concepts and techniques of recommender systems to build a strong foundation before the course starts.
Browse courses on
Recommender Systems
Show steps
-
Search for online tutorials on recommender systems
-
Review the topics covered in each tutorial
-
Identify the tutorials most suited to your learning style and goals
-
Complete at least two tutorials before the course begins
Five other activities
Expand to see all activities and additional details
Show all eight activities
Start a mini recommender system project
Show steps
Apply the concepts learned in the course to a practical project, solidifying your understanding and gaining hands-on experience.
Browse courses on
Project-Based Learning
Show steps
-
Define the scope and goals of your project
-
Choose a dataset and algorithms to use
-
Develop a prototype of your recommender system
-
Iterate on your design and improve the performance
-
Document your project and share your findings
Create a blog post or article on a recommender systems topic
Show steps
Deepen your understanding of a specific recommender systems topic by researching and writing about it, improving your communication and critical thinking skills.
Show steps
-
Choose a topic that interests you
-
Research the topic thoroughly
-
Organize your thoughts and create an outline
-
Write the blog post or article
-
Share your work with others
Participate in a recommender systems competition
Show steps
Challenge yourself by participating in a competition, gaining valuable experience and insights into the field of recommender systems.
Show steps
-
Find a suitable competition
-
Form a team or work individually
-
Develop a solution to the competition problem
-
Submit your solution and compete with others
Contribute to an open-source recommender systems project
Show steps
Gain practical experience and contribute to the wider recommender systems community by working on an open-source project.
Show steps
-
Find a suitable open-source project
-
Review the project's documentation and codebase
-
Identify an area where you can contribute
-
Make a pull request with your contribution
Mentor others in the field of recommender systems
Show steps
Reinforce your own understanding by sharing your knowledge and helping others learn about recommender systems.
Show steps
-
Identify opportunities to mentor others
-
Prepare materials and resources
-
Provide guidance and support
-
Reflect on your mentoring experience
Refresh your statistics skills
Show steps
Review the basics of statistics to strengthen your understanding of the mathematical foundations of recommender systems.
Browse courses on
Statistical Analysis
Show steps
Show steps
Show steps
- Review your lecture notes or textbooks
- Complete practice problems
- Attend a refresher course or workshop
- Join a study group or online forum for support
Refresh your knowledge of machine learning
Show steps
Review the fundamentals of machine learning to build upon your prior knowledge and enhance your understanding of recommender systems.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Review your lecture notes or textbooks
- Complete practice problems
- Attend a refresher course or workshop
- Join a study group or online forum for support
Explore online recommender systems tutorials
Show steps
Review the concepts and techniques of recommender systems to build a strong foundation before the course starts.
Browse courses on
Recommender Systems
Show steps
Show steps
Show steps
- Search for online tutorials on recommender systems
- Review the topics covered in each tutorial
- Identify the tutorials most suited to your learning style and goals
- Complete at least two tutorials before the course begins
Five other activities
Expand to see all activities and additional details
Show all eight activities
Start a mini recommender system project
Show steps
Apply the concepts learned in the course to a practical project, solidifying your understanding and gaining hands-on experience.
Browse courses on
Project-Based Learning
Show steps
Show steps
Show steps
- Define the scope and goals of your project
- Choose a dataset and algorithms to use
- Develop a prototype of your recommender system
- Iterate on your design and improve the performance
- Document your project and share your findings
Create a blog post or article on a recommender systems topic
Show steps
Deepen your understanding of a specific recommender systems topic by researching and writing about it, improving your communication and critical thinking skills.
Show steps
Show steps
Show steps
- Choose a topic that interests you
- Research the topic thoroughly
- Organize your thoughts and create an outline
- Write the blog post or article
- Share your work with others
Participate in a recommender systems competition
Show steps
Challenge yourself by participating in a competition, gaining valuable experience and insights into the field of recommender systems.
Show steps
Show steps
Show steps
- Find a suitable competition
- Form a team or work individually
- Develop a solution to the competition problem
- Submit your solution and compete with others
Contribute to an open-source recommender systems project
Show steps
Gain practical experience and contribute to the wider recommender systems community by working on an open-source project.
Show steps
Show steps
Show steps
- Find a suitable open-source project
- Review the project's documentation and codebase
- Identify an area where you can contribute
- Make a pull request with your contribution
Mentor others in the field of recommender systems
Show steps
Reinforce your own understanding by sharing your knowledge and helping others learn about recommender systems.
Show steps
Show steps
Show steps
- Identify opportunities to mentor others
- Prepare materials and resources
- Provide guidance and support
- Reflect on your mentoring experience
Career center
Learners who complete Recommender Systems Capstone will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts analyze data to extract meaningful insights and trends, which can be used to make informed decisions in a variety of industries. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides a strong foundation in data analysis and data mining, which are essential skills for Data Analysts. Additionally, the course's focus on experimental evaluation of algorithms can help Data Analysts develop the skills necessary to evaluate the performance of their own algorithms.
Information Technology Manager
Information Technology Manager
Information Technology (IT) Managers are responsible for planning, installing, and maintaining computer systems within an organization. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides IT Managers with the skills necessary to understand and implement recommender systems within their own organizations, which can help them improve customer satisfaction and engagement.
Software Developer
Software Developer
Software Developers design, develop, and maintain software applications. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides Software Developers with the skills necessary to develop recommender systems for a variety of applications, which can help them improve user experience and engagement.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models to solve complex problems. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides Machine Learning Engineers with the skills necessary to develop recommender systems using machine learning techniques, which can help them improve the accuracy and efficiency of their models.
Data Scientist
Data Scientist
Data Scientists use data to solve complex problems and make informed decisions. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides Data Scientists with the skills necessary to develop and evaluate recommender systems for a variety of applications, which can help them improve decision-making and customer engagement.
Product Manager
Product Manager
Product Managers are responsible for managing the development of new products. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides Product Managers with the skills necessary to understand the benefits and limitations of recommender systems, and how to use them to improve product development. Additionally, the course's focus on experimental evaluation of algorithms can help Product Managers develop the skills necessary to evaluate the performance of their own products.
Business Analyst
Business Analyst
Business Analysts use data to understand and improve business performance. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides Business Analysts with the skills necessary to use recommender systems to improve customer engagement and satisfaction.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to understand customer behavior and improve marketing campaigns. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides Marketing Analysts with the skills necessary to use recommender systems to improve the effectiveness of their marketing campaigns.
Consultant
Consultant
Consultants provide advice and expertise to businesses and organizations. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides Consultants with the skills necessary to understand the benefits and limitations of recommender systems, and how to use them to improve business performance. Additionally, the course's focus on experimental evaluation of algorithms can help Consultants develop the skills necessary to evaluate the performance of their own recommendations.
Project Manager
Project Manager
Project Managers plan and manage projects to ensure their successful completion. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides Project Managers with the skills necessary to understand the benefits and limitations of recommender systems, and how to use them to improve project outcomes. Additionally, the course's focus on experimental evaluation of algorithms can help Project Managers develop the skills necessary to evaluate the performance of their own projects.
Statistician
Statistician
Statisticians use data to analyze and interpret trends. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides Statisticians with the skills necessary to understand the benefits and limitations of recommender systems, and how to use them to improve data analysis and interpretation. Additionally, the course's focus on experimental evaluation of algorithms can help Statisticians develop the skills necessary to evaluate the performance of their own analysis.
UX Designer
UX Designer
UX Designers design user interfaces and experiences for websites and applications. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides UX Designers with the skills necessary to understand the benefits and limitations of recommender systems, and how to use them to improve the user experience of their own designs.
Web Developer
Web Developer
Web Developers design and develop websites. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides Web Developers with the skills necessary to understand the benefits and limitations of recommender systems, and how to use them to improve the user experience of their own websites.
Data Entry Clerk
Data Entry Clerk
Data Entry Clerks enter data into computer systems. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course may be helpful for Data Entry Clerks who want to learn more about data analysis and data mining.
Software Tester
Software Tester
Software Testers test software applications to find bugs and ensure that they work properly. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course may be helpful for Software Testers who want to learn more about data analysis and data mining.
For more career information including salaries, visit:
OpenCourser.com/course/6ilrhk/recommender
Reading list
We've selected six 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
Recommender Systems Capstone.
This comprehensive handbook provides a thorough overview of recommender systems, covering various algorithms, techniques, and applications. It serves as an excellent reference for understanding the fundamentals and advancements in the field.
Save
Explores the application of deep learning techniques in recommender systems. It presents advanced concepts and case studies, providing insights into the latest advancements and potential of deep learning in this domain.
Save
Offers a gentle introduction to recommender systems. It provides a comprehensive overview of the field, covering the history, techniques, and applications of recommender systems, making it suitable for beginners seeking a foundational understanding.
Focuses on the practical aspects of building and deploying recommender systems. It provides hands-on guidance, case studies, and real-world examples, making it valuable for practitioners seeking to implement and maintain effective recommender systems.
Explores adaptive recommender systems, which can adapt to changing user preferences and contexts. It covers various adaptation techniques, evaluation methods, and case studies, providing insights into the latest advancements in this area.
Focuses on the challenges and techniques involved in building recommender systems for large-scale datasets. It discusses distributed computing, scalability, and efficiency considerations, offering valuable insights for practitioners dealing with real-world data.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/6ilrhk/recommender
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Effort
1-3 weeks of study, 3-5 hours per week
Via
Coursera
Institution
University of Minnesota
Instructors
Michael D. Ekstrand
Joseph A Konstan
Language
English
Transcripts
Español
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中文
العربية
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This course belongs to a
collection
called
Recommender Systems. It's comprised of five courses:
Good to know
Suitable for individuals with prior experience in recommender systems or a related field seeking to enhance their understanding and skills
Taught by instructors with extensive knowledge and experience in recommender systems research and industry applications
Provides a comprehensive overview of recommender systems algorithms, evaluation techniques, and practical considerations
Develops critical thinking and analytical skills through a project-based approach involving analysis and design of recommender systems
Offers two tracks (standard and honors) to cater to varying levels of learner experience and interest
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