Introduction to Recommender Systems - University of MinnesotaCoursera
What you'll learn on the course
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. The algorithms we will study include content-based filtering, user-user collaborative filtering, item-item collaborative filtering, dimensionality reduction, and interactive critique-based recommenders. The approach will be hands-on, with six week projects, each of which will involve implementation and evaluation of some type of recommender. In addition to topical lectures, this course includes interviews and guest lectures with experts from both academia and industry. Beginning in February 2015, you will be able to earn a Verified Certificate by verifying your identity via a webcam and a government-issued ID. This option will provide formal recognition of your achievements in the course and includes the University of Minnesota logo. Before then, you can complete a “test run” of the exam. You can then re-take the exam after the Verified Certificate becomes available. For information regarding Verified Certificates, see https://courserahelp.zendesk.com/hc/en-us/articles/201212399-Verified-CertificatesOnline learning plays a key role in lifelong learning. In fact, a recent report by the United States Department of Education found that "the courses that include online education (either completely virtual or blended learning) produce, on average, much stronger learning outcomes for students courses They are conducted exclusively in person. Based on an approach developed by educational psychologist Benjamin Bloom, the mastery learning helps students to fully understand a subject before moving on to a more advanced. In Coursera, usually we give an answer immediately to the concepts that the student does not understand feedback. In many cases, we offer random versions of assessments for the student to return to school and retrying until mastered the concept.