Recommender Systems 专项课程

于 Aug 27 开始

Recommender Systems 专项课程

Master Recommender Systems。Learn to design, build, and evaluate recommender systems for commerce and content.

本专项课程介绍

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.

制作方:

courses
5 courses

按照建议的顺序或选择您自己的顺序。

projects
项目

旨在帮助您实践和应用所学到的技能。

certificates
证书

在您的简历和领英中展示您的新技能。

课程
Intermediate Specialization.
Some related experience required.
  1. 第 1 门课程

    Introduction to Recommender Systems: Non-Personalized and Content-Based

    计划开课班次:Aug 27
    课程学习时间
    4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track.
    字幕
    English

    课程概述

    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 su
  2. 第 2 门课程

    Nearest Neighbor Collaborative Filtering

    计划开课班次:Aug 20
    字幕
    English

    课程概述

    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 similar t
  3. 第 3 门课程

    Recommender Systems: Evaluation and Metrics

    计划开课班次:Aug 27
    字幕
    English

    课程概述

    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, pr
  4. 第 4 门课程

    Matrix Factorization and Advanced Techniques

    计划开课班次:Aug 20
    字幕
    English

    课程概述

    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 recomm
  5. 第 5 门课程

    Recommender Systems Capstone

    计划开课班次:Oct 1
    课程学习时间
    1-3 weeks of study, 3-5 hours per week
    字幕
    English

    毕业项目介绍

    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 case

制作方

  • University of Minnesota

    The University of Minnesota has been a leader in recommender systems since developing GroupLens, the first automated recommender system in 1993. Today the University continues that leadership with leading research on recommender algorithms, applications, and evaluation.

    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.

  • Joseph A Konstan

    Joseph A Konstan

    Distinguished McKnight Professor and Distinguished University Teaching Professor
  • Michael D. Ekstrand

    Michael D. Ekstrand

    Assistant Professor

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