Skip to main content 18.337: Parallel Computing and Scientific Machine Learning
Table of contents
- Course Info
- Realistic Prerequisites
- Subject Matter
- Course Staff
- Lectures
- Problem Sets
- Exams
- Resources
- Grading
- 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.
Lectures
- 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.
Exams
Resources
- Students used notes from the SciML website by Chris Rackauckas.
- Students also used lecture videos by Chris Rackauckas on YouTube.
Grading
- Students felt that grading was reasonable.
Advice to Future Students
- “Anything useful can be found on the course GitHub.”
- “Subject material had the potential to be so so good, but the class was so disorganized.”