Personalized Machine Learning

NIE-PML

Personalized machine learning (PML) is a sub-field of machine learning that aims to create models and predictions based on the unique characteristics and behaviors of individual entities. While PML is commonly used in applications such as recommender systems, which recommend items to users based on their personal interests, its principles can be applied to a wide range of other fields, including education, medicine, and chemical engineering. In this course, we will explore the latest PML methods from theoretical, algorithmic, and practical perspectives. Specifically, we will focus on cutting-edge models that are of interest to both the research and commercial communities.

Prerequisites

The knowledge of basic calculus, linear algebra, probability theory and basics of machine learning is assumed. So a student that completed at least one course of block Perception or CoreML has the requisites to attend the course. Suitable for master students.

Details

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