Advanced Recommender Systems
- Offered byCoursera
Advanced Recommender Systems at Coursera Overview
Duration | 15 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Advanced Recommender Systems at Coursera Highlights
- 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 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 Subtitles: English
Advanced Recommender Systems at Coursera Course details
- In this course, you will see how to use advanced machine learning techniques to build more sophisticated recommender systems. Machine Learning is able to provide recommendations and make better predictions, by taking advantage of historical opinions from users and building up the model automatically, without the need for you to think about all the details of the model.
- At the end of this course, you will learn how to manage hybrid information and how to combine different filtering techniques, taking the best from each approach. You will know how to use factorization machines and represent the input data accordingly. You will be able to design more sophisticated recommender systems, which can solve the cross-domain recommendation problem. You will also learn how to identify new trends and challenges in providing recommendations in a range of innovative application contexts.
- This 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.
Advanced Recommender Systems at Coursera Curriculum
ADVANCED COLLABORATIVE FILTERING
Course overview and welcome by the instructor
Welcome by the instructor - module overview
Item-Based CF as Optimization Problem
SLIM
Recap by the instructor
Bayesian Probabilistic Ranking
Conclusions by the instructor
Course Syllabus
Credits & Aknowledgements
Module 1 Advanced - Graded Assessment
SINGULAR VALUE DECOMPOSITION TECHNIQUES - SVD
Welcome by the instructor
Matrix Factorization
Funk SVD
SVD++
Recap by the instructor
Asymmetric SVD
Pure SVD
Conclusions by the instructor
Module 2 Advanced - Graded Assessment
HYBRID AND CONTEXT AWARE RECOMMENDER SYSTEMS
Welcome by the instructor
Hybrid Recommender Systems
Linear Combination
List Combination
Pipelining
Recap by the instructor
Merging Models
CF with Side Information
Context-Aware Recommender Systems
Conclusions by the instructor
Module 3 Advanced - Graded Assessment
FACTORIZATION MACHINES
Welcome by the instructor
Factorization Machines
Recap by the instructor
Explaining FM's Model
Extending the model
Solving the imbalance problem
Conclusions by the instructor
Module 4 Advanced - Graded Assessment
Recsys Challenge (Honors)
The RecSys Challenge
Advanced Recommender Systems at Coursera Admission Process
Important Dates
Other courses offered by Coursera
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