Ementorhub

What you'll learn

You will be able to use some machine learning 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.

Skills you'll gain

Performance Tuning

Supervised Learning

Applied Machine Learning

Unsupervised Learning

Feature Engineering

Artificial Intelligence and Machine Learning (AI/ML)

Data Analysis

Algorithms

Dimensionality Reduction

Machine Learning

Predictive Modeling

There are 5 modules in this course

At the end of the Advanced Recommender Systems, you will know how to manage hybrid information and how to combine different filtering techniques, taking the best from each approach. More, you will know how to use factorisation machines and represent the input data accordingly and be able to design more sophisticated recommender systems, which can solve the cross-domain recommendation problem. The course leverages two important EIT Digital Overarching Learning Outcomes (OLOs), related to your creativity and innovation skills. In trying to design a new recommender system you need to think beyond boundaries and try to figure out how you can improve the quality of the outcomes. You should also be able to use knowledge, ideas and technology to create new or significantly improved recommendation tools to support choice-making processes and solve real-life problems in complex and innovative scenarios.

SINGULAR VALUE DECOMPOSITION TECHNIQUES - SVD

HYBRID AND CONTEXT AWARE RECOMMENDER SYSTEMS

FACTORIZATION MACHINES

Recsys Challenge (Honors)