Repo for CMU course - Machine Learning in Practice

Each folder represents resources and notes for each lecture. Students should submit notes to the appropriate folder via a PR.

As Machine Learning and Artificial Intelligence methods have become common place in both academic and industry environments the majority of resources have focused on methods and techniques for applications. However, there are many considerations that must be addressed when deploying such techniques into practice (or production). The purpose of this course is to cover topics relevant to building a machine learning systems deployed into operations. Such systems have technical requirements including data management, model development, and deployment. However, business/organizational impacts must also be considered. Machine learning systems can be expensive to produce and operate. Students will learn about trade-offs in design, implementation, and expected value. After completing this course, students will:

1. Have the ability to deploy products with machine learning and AI components
2. Understand how to implement data pipelines and data engineering systems
3. Calculate the approximate value provided by a machine learning system to an organization
4. Understand how to continually assess the value and quality of a deployed machine learning system

Prerequisites: understanding of basic machine learning concepts (i.e. supervised/unsupervised learning).

• Group 1 (March 15th | Lecture 1)

  • Steve Choi
  • David Good

• Group 2 (March 17th | Lecture 2)

  • Kedar Deshpande
  • Wei Ziyuan

• Group 3 (March 22nd | Lecture 3)

  • Connie He
  • Huilin Xiong
  • Phoebe Li

• Group 4 (March 24th | Lecture 4)

  • Aditya Bindra
  • Akshay Bahadur
  • Naman Arora

• Group 6 (March 31rd | Lecture 6)

  • Jiayi Huang

  • Xiaoting Wang

  • Chunying Li