About this course: This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations. After completing this course, you will be able to compute a variety of recommendations from datasets using basic spreadsheet tools, and if you complete the honors track you will also have programmed these recommendations using the open source LensKit recommender toolkit. In addition to detailed lectures and interactive exercises, this course features interviews with several leaders in research and practice on advanced topics and current directions in recommender systems.
Who is this class for: This course is appropriate for learners who have a basic understanding of statistics. It can be useful both for those exploring applied machine learning and data mining, and for those focused on technology-supported marketing and commerce.
Taught by: Joseph A Konstan, Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
Taught by: Michael D. Ekstrand, Assistant Professor
Dept. of Computer Science, Boise State University
| Basic Info |
Course 1 of 5 in the
Recommender Systems Specialization.
|
| Level | Intermediate |
| Commitment | 4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track. |
| Language | English |
| Hardware Req | For honors track must be able to run substantial computations using Java (e.g., 4GB or more ram). |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
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| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
The course was easy to understand. but i find the slides not much of help.
Really good course to get started with recommendation systems!
Excelente curso, presenta una vista amplia de técnicas para la implementación de sistemas de recomendación, lo recomiendo totalmente.
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There is a need to have this course in Python or some other statistical programming language. Simple reason is that a lot of budding data scientists are not coming from CS background and dont have necessary skillset in Java. Else the course is good.