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英語
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共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における5の3コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
約7時間で修了
英語
字幕:英語

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ミネソタ大学(University of Minnesota) ロゴ

ミネソタ大学(University of Minnesota)

シラバス - 本コースの学習内容

1

1

14分で修了

Preface

14分で修了
2件のビデオ (合計14分)
2件のビデオ
The Goals of Evaluation10 分
2時間で修了

Basic Prediction and Recommendation Metrics

2時間で修了
5件のビデオ (合計57分), 1 reading, 1 quiz
5件のビデオ
Prediction Accuracy Metrics12 分
Decision Support Metrics16 分
Rank-Aware Top-N Metrics18 分
Assignment Intro Video2 分
1件の学習用教材
Metric Computation Assignment Instructions10 分
1の練習問題
Basic Prediction and Recommendation Metrics Assignment42 分
2

2

2時間で修了

Advanced Metrics and Offline Evaluation

2時間で修了
6件のビデオ (合計76分), 1 reading, 2 quizzes
6件のビデオ
Additional Item and List-Based Metrics18 分
Experimental Protocols13 分
Unary Data Evaluation11 分
Temporal Evaluation of Recommenders (Interview with Neal Lathia)12 分
Programming Assignment Introduction8 分
1件の学習用教材
Evaluating Recommenders10 分
2の練習問題
Offline Evaluation and Metrics Quiz22 分
Programming Assignment Quiz28 分
3

3

1時間で修了

Online Evaluation

1時間で修了
4件のビデオ (合計66分)
4件のビデオ
Usage Logs and Analysis10 分
A/B Studies (Field Experiments)11 分
User-Centered Evaluation (Interview with Bart Knijnenburg)25 分
1の練習問題
Online Evaluation Quiz8 分
4

4

1時間で修了

Evaluation Design

1時間で修了
3件のビデオ (合計31分), 2 readings, 1 quiz
3件のビデオ
Case Examples17 分
Assignment Intro Video2 分
2件の学習用教材
Intro to Assignment: Evaluation Design Cases10 分
Quiz Debrief10 分
1の練習問題
Assignment: Evaluation Design Cases12 分

レビュー

RECOMMENDER SYSTEMS: EVALUATION AND METRICS からの人気レビュー

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レコメンダシステム専門講座について

A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space. This Specialization is designed to serve both the data mining expert who would want to implement techniques like collaborative filtering in their job, as well as the data literate marketing professional, who would want to gain more familiarity with these topics. The courses offer interactive, spreadsheet-based exercises to master different algorithms, along with an honors track where you can go into greater depth using the LensKit open source toolkit. By the end of this Specialization, you’ll be able to implement as well as evaluate recommender systems. The Capstone Project brings together the course material with a realistic recommender design and analysis project....
レコメンダシステム

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