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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/matrix-factorization
课程评论：没有评论
课程名称：矩阵分解与高级技术 课程概述：本课程将教授各种矩阵分解和混合机器学习技术，以构建推荐系统。课程从基础的矩阵分解开始，让学员理解构建推荐系统的直观思路和实践细节，旨在通过降低用户-产品偏好空间的维度来优化推荐效果。接着，将学习结合不同算法优势的混合推荐技术，构建更强大的推荐系统。 课程大纲： 1. 前言：介绍课程内容与学习目标。 2. 矩阵分解（第一部分）：这是一个为期两周的模块，集中讲解矩阵分解推荐技术，包括作业和测验（均在第二周截止），以及荣誉作业（同样在第二周截止）。请合理安排学习进度，第一周就应开始作业，以便在两周内完成。 3. 矩阵分解（第二部分）：继续深入学习矩阵分解技术。 4. 混合推荐系统：这是一个为期两周的三部分模块，介绍混合和机器学习推荐算法及高级推荐技术，包括测验（在第二周截止）和荣誉作业。学习进度同样需要合理安排，以便在两周内完成荣誉轨道。 5. 高级机器学习：探讨更复杂的机器学习方法。 6. 高级主题：涵盖推荐系统领域的前沿主题。 本课程适合希望深入了解推荐系统构建的学员，特别是对实现高效的算法有兴趣的学习者。
Part: 1
Title:Preface
Description:
Part: 2
Title:Matrix Factorization (Part 1)
Description: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 difficult to finish in two weeks unless you start the assignments during the first week.
Part: 3
Title:Matrix Factorization (Part 2)
Description:
Part: 4
Title:Hybrid Recommenders
Description: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 carefully -- it will be difficult to finish the honors track in two weeks unless you start the assignments during the first week.
Part: 5
Title:Advanced Machine Learning
Description:
Part: 6
Title:Advanced Topics
Description:
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.
