Michael Ekstrand




Michael D Ekstrand is a Ph.D student in computer science specializing in human-computer interaction. His interests lies in tools for helping humans deal with information in a need- and task-centered fashion, programming languages that make it easier for people to produce robust software, and computer science education.

More info: http://www.linkedin.com/in/elehack

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May 29th 2017

In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user.

Average: 6.5 (2 votes)
May 22nd 2017

This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations.

Average: 7.5 (4 votes)
Sep 5th 2016

Recommender systems have changed the way people find products, information, and even other people. They study patterns of behavior to know what someone will prefer from among a collection of things he has never experienced. The technology behind recommender systems has evolved over the past 20 years into a rich collection of tools that enable the practitioner or researcher to develop effective recommenders. We will study the most important of those tools, including how they work, how to use them, how to evaluate them, and their strengths and weaknesses in practice.

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