18-847F Special Topics in Computer Systems:
Foundations of Cloud and Machine Learning Infrastructure

Course Overview

The objective of this graduate-level seminar course is to introduce students to algorithms in large-scale computing and machine learning. You will read, present and critique a curated set of research papers from both theory and systems. Each class will comprise of presentation and discussion of two research papers.

The first half of the course will cover distributed computing frameworks and scheduling and load balancing policies used in them. In the context of distributed storage, we will discuss coding-theoretic techniques used to improve availability and repair failed nodes. The second half of the course will focus on machine learning infrastructure. We will cover distributed SGD, federated learning and hyper-parameter tuning.

For more information - Course Syllabus

Prerequisites

None. Basic knowledge of probability and linear algebra is encouraged

Grading Policy

Grades will be based on the following components:

• Homework (45%)
• In-class Quizzes (35%)
• Class Presentation(s) (10%)
• Class Participation (10%)

You are allowed to take 3 grace days for this course. You can take at the most 1 grace day for an assignment.

Tentative Schedule