开始时间: 10/13/2018 持续时间: Unknown
This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative techniques. Designed to serve both the data mining expert and the data literate marketing professional, the courses offer interactive, spreadsheet-based exercises to master different algorithms along with an honors track where learners can go into greater depth using the LensKit open source toolkit. A Capstone Project brings together the course material with a realistic recommender design and analysis project.
Introduction to Recommender Systems: Non-Personalized and Content-Based
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
Nearest Neighbor Collaborative Filtering
In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with simila
Recommender Systems: Evaluation and Metrics
In this course you will learn how to evaluate recommender systems. You will gain familiarity with several families of metrics, including ones to measure prediction accuracy, rank accuracy, decision-support, and other factors such as diversity,
Matrix Factorization and Advanced Techniques
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 rec
Recommender Systems Capstone
Upcoming session: Dec 24
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 ca
明尼苏达大学的推荐系统专项课程系列（Recommender Systems Specialization），这个系列由4门子课程和1门毕业项目课程组成，包括推荐系统导论，最近邻协同过滤，推荐系统评价，矩阵分解和高级技术等，感兴趣的同学可以关注：Master Recommender Systems-Learn to design, build, and evaluate recommender systems for commerce and content.