You should take this course if you have an interest in machine learning and the desire to engage with it from a theoretical perspective. Through a combination of classic papers and more recent work, you will explore automated decision-making from a computer-science perspective. You will examine efficient algorithms, where they exist, for single-agent and multi-agent planning as well as approaches to learning near-optimal decisions from experience. At the end of the course, you will replicate a result from a published paper in reinforcement learning.
Computing for Data Analysis
The modern data analysis pipeline involves collection, preprocessing, storage, analysis, and interactive visualization of data. The goal of this course, part of the Analytics: Essential Tools and Methods MicroMasters program,…