Recommender Systems 專項課程

開始日期 Jul 16

Recommender Systems 專項課程

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

本專項課程介紹

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.

製作方:

courses
5 courses

按照建議的順序或選擇您自己的順序。

projects
項目

旨在幫助您實踐和應用所學到的技能。

certificates
證書

在您的簡歷和領英中展示您的新技能。

課程
Intermediate Specialization.
Some related experience required.
  1. 第 1 門課程

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

    當前班次:Jul 16
    課程學習時間
    4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track.
    字幕
    English

    課程概述

    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 門課程

    Nearest Neighbor Collaborative Filtering

    計劃開課班次:Jul 23
    字幕
    English

    課程概述

    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 門課程

    Recommender Systems: Evaluation and Metrics

    當前班次:Jul 16
    字幕
    English

    課程概述

    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 門課程

    Matrix Factorization and Advanced Techniques

    計劃開課班次:Jul 23
    字幕
    English

    課程概述

    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 門課程

    Recommender Systems Capstone

    計劃開課班次:Oct 1
    課程學習時間
    1-3 weeks of study, 3-5 hours per week
    字幕
    English

    畢業項目介紹

    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

製作方

  • 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

FAQs