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 |
Well prepared course. In-depth lecture. Easy to follow even when listening only. The course lectures is very detailed, and that is one thing I really liked. The videos does feel a bit long, and maybe we can chop it to smaller sub-topics.
The interviews are very interesting and show a glimpse of broader universe of recommendation system. However, the concepts explained in the interview is a bit hard to follow, as there is no accompanying presentation materials and it jumps to detailed content with little context
The regular exercise feels very easy but helpful to make the concepts concrete. The Honors programming exercise looks interesting & challenging, but it seems too hard for someone with no programming background. I am also learning Python in parallel, so I decided to drop it to avoid learning 2 languages in parallel.
Excellent course taught in simple language.
An excellent in-depth introduction into the concepts around recommendation systems!
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great!! Let me better understand the research and practical fields!