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.
Why this Course
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 modern tools.
By the end of this Specialization, you’ll be able to implement as well as evaluate recommender systems. The Project brings together the course material with a realistic recommender design and analysis project.
Students will learn to:
- Students will learn the concept of recommender system and its use in web applications.
- Students will acquire knowledge about optimization and learn various collaborative filtering methods
Students will be able to:
- Learn about advanced topics and current applications of recommender systems in other realms such as mobile computing.
- Understand the basic concepts of recommender systems
- Solve mathematical optimization problems pertaing to recommender systems
- Implement machine-learning and data-mining algorithms in recommender systems data sets.
- Carry out performance evaluation of recommender systems based on various metrics
- Design and implement a simple recommender system.
Modules at a Glance
- Introduction and basic taxonomy of recommender systems
- Overview of data mining
- Collaborative Filtering
- Regularization and overfitting
- Advanced Collaborative Filtering
- Context awareness and Learning principles
- Applications of RSs for content media, social media and communities Music
- Video Recommender System
How to Apply
The Certificate Courses are offered twice a year during even and odd semesters. Students interested can take admission in the offered certificate courses at the beginning of the semester.
Students from other institutions can also take admission in these certificate courses at the beginning of even and odd semesters by contacting the concerned teacher incharge and by paying the required fees for the course.
Any individual who has successfully completed HSC (12TH) in any Stream. Or Any student Studying in KES Shroff College in different programmes.
The Certificate will be awarded to the student who
- will attend the lectures as per college norms minimum 60% attendance is must to appear for the examination.
- will successfully pass the examination with a minimum 40% .