Big Data and ML — Spring 2023

Instructors
Help? Campuswire (use code 0187 to join)
When? Wednesday 7:10 PM to 9:10 PM
Where? Warren Weaver Hall 312 and Zoom
Hours
Day Time Who Where
TBD TBD Panda 60FA Room 405

Course Aims

The aim of this course is to spend a semester studying distributed tracing and looking at how it is used today, and how it can be expanded. The course is going to be somewhat project heavy. Throughout the semester we will be reading papers and also following text from Distributed Tracing in Practice a recent book on the topic that can be freely accessed online through the library.

Tentative Schedule and Syllabus

Date Topic & Readings Other
01/25 Introduction: Course Mechanics and Overview
02/01
  • Chapter 1 DTP
  • Dapper
  • Pick a language and follow instructions at OpenTelemetry to collect an initial set of logs.
02/08
    02/15
      02/22
        03/01
          03/08 MIDTERM MIDTERM
          03/15 SPRING BREAK
          03/22
            03/29
              04/05
              04/12
                04/19
                  04/26
                  05/03
                  05/08 Final project presentation

                  Grading

                  Grading will be based on quality of work, and presentation. The grade breakdown is as follows (this might change until the beginning of semester):

                  • 30% for three homework assignments. These are checkpoints that require you to collect the work you have done for class and submit them, rather than projects you need to do from scratch.
                  • 15% for class participation. This 15% will be split into two portions:
                    1. 5% for participating in in-class discussion.
                    2. 10% for helping other students on Campuswire, writing tutorials or notes, and calss presentations.
                  • 25% for the final project: This should be done in groups of 2 or 3 people.
                  • 10% Midterm, 20% Final exam. By default both are in-person.