18.337: Parallel Computing and Scientific Machine Learning

Table of contents

  1. Course Info
  2. Realistic Prerequisites
  3. Subject Matter
  4. Course Staff
  5. Lectures
  6. Problem Sets
  7. Exams
  8. Resources
  9. Grading
  10. Advice to Future Students

Course Info

Class Size 78
Hours/Week 10.1 (33 responses)
Instructors Alan Edelman
Overall Rating 3.8/7.0

Realistic Prerequisites

  • 18.03 is a hard prerequisite.
  • Students recommended a coding background equivalent to 6.101.

Subject Matter

  • The course includes some theory, but leans more towards applied content.
  • Students say this course gives a great foundation for further work in parallel computing or machine learning.

Course Staff

  • Students found the professor was not responsive.
  • Some students mentioned the course staff adding additional office hours at the students’ requests.


  • Students found lectures to be somewhat disorganized.
  • Students used online notes and YouTube videos to learn the course material.

Problem Sets

  • Students found the problem sets interesting, and doable.
  • Students found problem sets confusing and poorly worded, often needing clarification via Piazza.


  • There were no exams.


  • Students used notes from the SciML website by Chris Rackauckas.
  • Students also used lecture videos by Chris Rackauckas on YouTube.


  • Students felt that grading was reasonable.

Advice to Future Students

  1. “Anything useful can be found on the course GitHub.”
  2. “Subject material had the potential to be so so good, but the class was so disorganized.”