About this Course

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Intermediate Level

Basic notions of linear algebra

Approx. 12 hours to complete
English

What you will learn

  • You'll be able to build a basic recommender system.

  • You'll be able to choose the family of recommender systems that best suits the kind of input data, goals and needs.

  • You'll learn how to identify the correct evaluation activities to measure the quality of a recommender system, based on goals and needs.

  • You'll be able to point out benefits and limits of different techniques for recommender systems in different scenarios.

Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level

Basic notions of linear algebra

Approx. 12 hours to complete
English

Offered by

Placeholder

EIT Digital

Syllabus - What you will learn from this course

Week
1

Week 1

3 hours to complete

BASIC CONCEPTS

3 hours to complete
11 videos (Total 29 min), 2 readings, 2 quizzes
11 videos
Welcome by the instructor - module overview1m
Introduction to Recommender Systems3m
Taxonomy of Recommender Systems6m
Item-Content Matrix1m
User-Rating Matrix2m
Inferring Preferences3m
Recap by the instructor1m
Non-personalized Recommender Systems3m
Global Effects2m
Conclusions by the instructor1m
2 readings
Course Syllabus10m
Credits & Acknowledgements5m
1 practice exercise
Module 1 - Graded Assessment45m
Week
2

Week 2

3 hours to complete

EVALUATION OF RECOMMENDER SYSTEMS

3 hours to complete
12 videos (Total 35 min)
12 videos
Quality of Recommender Systems1m
Quality Indicators3m
Online Evaluation Techniques3m
Offline Evaluation Techniques2m
Dataset Partitioning4m
Overfitting1m
Recap by the instructor1m
Error Metrics3m
Classification Metrics4m
Ranking Metrics7m
Conclusions by the instructor1m
1 practice exercise
Module 2 - Graded Assessment40m
Week
3

Week 3

3 hours to complete

CONTENT-BASED FILTERING

3 hours to complete
9 videos (Total 19 min)
9 videos
Content-based Filtering2m
Cosine Similarity4m
Matrix Notation1m
K-Nearest Neighbours2m
Recap by the instructor1m
Improving the ICM2m
TF-IDF2m
Conclusions by the instructor54s
1 practice exercise
Module 3 - Graded Assessment45m
Week
4

Week 4

3 hours to complete

COLLABORATIVE FILTERING

3 hours to complete
9 videos (Total 42 min)
9 videos
Collaborative Filtering2m
User-based CF13m
Recap by the instructor1m
Item-based CF12m
User-based vs. Item-based2m
Model-based vs. Memory-based5m
Recommendation as Association Rules2m
Conclusions by the instructor48s
1 practice exercise
Module 4 - Graded Assessment30m

Frequently Asked Questions

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