About this Course

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Intermediate Level

Basic knowledge of recommender systems. Some acquaintance with the most basic programming languages (like Python). Basic notions of linear algebra.

Approx. 15 hours to complete
English

What you will learn

  • You will be able to use some machine learning and neural network techniques, in order to build more sophisticated recommender systems.

  • You will learn how to combine different basic approaches into a hybrid recommender system, in order to improve the quality of recommendations.

  • You will know how to integrate different kinds of side information (about content or context) in a recommender system.

  • You'll learn how to use factorization machines and represent the input data, mixing together different kinds of filtering techniques.

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

Basic knowledge of recommender systems. Some acquaintance with the most basic programming languages (like Python). Basic notions of linear algebra.

Approx. 15 hours to complete
English

Offered by

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EIT Digital

Placeholder

Politecnico di Milano

Syllabus - What you will learn from this course

Week
1

Week 1

3 hours to complete

ADVANCED COLLABORATIVE FILTERING

3 hours to complete
7 videos (Total 20 min), 2 readings, 3 quizzes
Week
2

Week 2

2 hours to complete

SINGULAR VALUE DECOMPOSITION TECHNIQUES - SVD

2 hours to complete
8 videos (Total 26 min)
Week
3

Week 3

3 hours to complete

HYBRID AND CONTEXT AWARE RECOMMENDER SYSTEMS

3 hours to complete
10 videos (Total 24 min)
Week
4

Week 4

3 hours to complete

FACTORIZATION MACHINES

3 hours to complete
7 videos (Total 20 min)

Frequently Asked Questions

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