About this course: 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.
Created by: University of Minnesota
Taught by: Michael D. Ekstrand, Assistant Professor
Dept. of Computer Science, Boise State University
Taught by: Joseph A Konstan, Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
| Basic Info |
Course 4 of 5 in the
Recommender Systems Specialization.
|
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Preface
1 video
WEEK 2
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 difficu...
5 videos, 1 reading
WEEK 3
Matrix Factorization (Part 2)
2 videos, 2 readings
Graded: Matrix Factorization Assignment Part l
Graded: Matrix Factorization Assignment Part ll
Graded: Matrix Factorization Assignment Part lll
Graded: Matrix Factorization Quiz
Graded: Programming SVD
Graded: SVD Programming Eval Quiz
WEEK 4
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 caref...
6 videos
WEEK 5
Advanced Machine Learning
3 videos
WEEK 6
Advanced Topics
7 videos, 1 reading
Graded: Hybrid and Advanced Techniques Quiz
Graded: Programming Hybrids and Learning-to-Rank
Graded: Honors Hybrid Assignment Evaluation Quiz
FAQs
How It Works
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from Your Peers
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Certificates
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
Creators
University of Minnesota
The University of Minnesota is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation’s most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations.
Pricing
| 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 |
Ratings and Reviews
Coursera provides universal access to the world’s best education, partnering with top universities and organizations to offer courses online.
© 2017 Coursera Inc. All rights reserved.
great courses! They invite a lot of interviews to let me understand the sea of recommend system!