Course Description

This is a course in how to utilize data: infer, predict, coerce, and classify. We will cover a large breadth of material, spanning supervised and unsupervised learning, recommender systems, and Bayesian modelling, to a high level of mathematical rigor. Upon successful completion of the course, students should be fully equipped to enter industry as a data scientist, read active research in the field of Machine Learning, and approach huge (data and otherwise) problems seen in the real world.

Additionally, another goal of this course is to become comfortable using Amazon Web Services and GitHub as these tools are extremely prevalent in industry and academia when developing and deploying models. To that end, all code for homework and your final project will be hosted on GitHub.

Summary of Goals

  • Gain a comprehensive view of machine learning as an academic discipline and understand the mathematics behind it.

  • Be able to read recent academic papers in the machine learning literature and apply those algorithms and concepts to real world problems.

  • Become comfortable with industry and academia standard tools (such as AWS and GitHub) and be able to find and work with large, public datasets.