(Course number: VIP 2601/3601/3602/4601/4602/6600/6603 VWP) DRAFT: As of Wednesday, August 21, 2024, this syllabus is still in draft form. A final version should be available no later than Thursday, August 22. The aim of Team Phoenix, a Vertically Integrated Project (VIP), is to help you learn the nuts and bolts of high-performance computing or supercomputing. When you "supercompute," you use networks of computers to solve computational problems that are either too big or too slow to solve on a single machine. Examples of such problems include modeling the climate; simulating what happens when two black holes collide;

Dear student, In the opening of his book, The nature of mathematical modeling, Neil Gershenfeld poses the following questions (slightly edited by me): How would you: Understand how the sound of a violin works? Synthesize the sound of a violin? Understand why traffic jams occur on a highway? Relieve traffic jams on a highway? Understand why it's raining today? Predict, or maybe affect, whether it will rain tomorrow? Understand how people move through a supermarket checkout? Help people move through a checkout faster?

The goal of this project is to study a randomized approximation algorithm for solving the all-pairs shortest paths (APSP) problem. In APSP, you are given a graph and you would like to find, for every pair of vertices, the shortest path that connects them and/or the length of such a path. At Georgia Tech, you would most likely encounter the APSP problem in a class like CS 3510, where you "meet" the classic textbook algorithm for it known as the Floyd-Warshall algorithm. But whereas FW calculates exact paths and path lengths, in this project we will care only about estimating the shortest paths and path lengths. The project involves some graph theory, linear algebra, probability, and abstract algebra. It's ideal if you've already taken classes in these subjects, but the problem is "simple enopugh" that you can learn what you need as you go. For that, I've compiled some warm-up material, below. There are four suggested warm ups; once you've made it through these, I think we can start! Warm-up 1: Triangle counting, where graphs meet linear algebra via matrix multiplication Warm-up 2: A simple method to approximate matrix multiplication Warm-up 3: Approximate triangle counting via approximate matrix multiplication Warm-up 4: Exact APSP (all-pairs shortest paths)

This syllabus is official. It was last updated on August 23, 2023. Revision history: [c730c59] August 23, 2023: Added proctoring policy under How do the exams work? [bb17c37] August 21, 2023: First official release. TL;DR: Learn what UTC is — there will be no forgiveness for misunderstanding the due date/time convention! Try the programming problems suggested under "prerequisites." Communicate with us via the online discussion forum (Piazza) or office hours, not email! We make all announcements and updates via to the forum. Read the schedule. Bookmark it in your browser. Re-read it. Check it frequently.

Dear student, In the opening of his book, The nature of mathematical modeling, Neil Gershenfeld poses the following questions (slightly edited by me): How would you: Understand how the sound of a violin works? Synthesize the sound of a violin? Understand why traffic jams occur on a highway? Relieve traffic jams on a highway? Understand why it's raining today? Predict, or maybe affect, whether it will rain tomorrow? Understand how people move through a supermarket checkout? Help people move through a checkout faster?

(Course number: VIP 2601/3601/3602/4601/4602/6600/6603 VWP) This schedule is marked as DRAFT as of January 8, 2023, and will be finalized by the first day of class, January 11. This class meets on Wednesdays from 3:30-4:20 pm. The first day of class is January 11, 2023, in VL 465. The aim of Team Phoenix, a Vertically Integrated Project (VIP), is to help you learn the nuts and bolts of high-performance computing or supercomputing. When you "supercompute," you use networks of computers to solve computational problems that are either too big or too slow to solve on a single machine. Examples of such problems include modeling the climate; simulating what happens when two black holes collide;

This syllabus is up-to-date as of May 17, 2021 (the first day of summer session). TL;DR Learn what UTC is — there will be no forgiveness for misunderstanding the due date/time convention. Try the programming problems suggested under "prerequisites." All course communication is by Piazza, not email. The professor expects you to get 100% on the assignments; it's the exams that are the real assessments of where you stand. Learn what an "MRE" is (minimal reproducible example, not meal ready-to-eat, although we'd never begrudge one the latter).

This syllabus is up-to-date as of April 22, 2021. Logistics due to the on-going pandemic: This class is virtual but offered in a remote-synchronous mode. We will hold live sessions, which you are encouraged to attend. However, we will record these sessions, too, in case you miss class, reside in a timezone that makes attending the live sessions difficult or impossible, or otherwise need to or prefer to watch "offline." Although these sessions may be viewed at any time, the class will proceed at a particular place, with assignments due on a particular schedule as explained below. Meeting time (synchronous, but recorded): Tuesdays and Thursdays from 2-3:15 pm ET Connection method: See the course Canvas site for details. Instructor: Professor Richard Vuduc

This page summarizes the main activities and tasks of teaching assistants for CSE 6040, the Introduction to Computing for Data Analysis class that is part of Georgia Tech's MS in Analytics program. Activities and Tasks The expected workload is about 10 hours per week, though that can be "bursty" (some weeks with more than that and many with fewer). Monitor Piazza forums and help handle individual student issues. Depending on the number of TAs, I expect to have daily "shifts" during "extended business hours" (roughly, 9 am to 9 pm), where you only need to be active one day per week. (Plan to log in 3-4 times on your assigned day to answer or log open questions, and follow up on your questions as needed throughout the week.) Participate in a weekly staff meeting (1 hour in duration) Co-host weekly office hours (1 hour in duration, in pairs, but not every week depending on the total number of TAs) Evaluate exam problems -- You'll be asked either to help write or help stress-test/debug an exam problem. There are three exams: Midterms 1 & 2 and the Final.

# Schedule - CSE 6040/x: Intro to Computing for Data Analysis, Fall 2019 ## Module 0 | Week | Dates | | Topic | Assignments Due (11:59 UTC) | |--------|-------|-------|---------------------------------------------------|--------------------------------------------------------------------------| | 0 | 8/19 | 8/25 | 0+1: Intro + Python Review | Notebook 0: due 8/27 (ungr

# Syllabus (Final) - CSE 6040/x: Intro to Computing for Data Analysis, Fall 2019 This course is a hands-on introduction to programming techniques relevant to data analysis and machine learning. Programming exercises use Python and SQL. It assumes some prior programming experience (not necessarily in Python), but asks you to review and extend that experience in the context of data analysis. * Instructor and course creator: [Professor Richard (Rich) Vuduc](https://vuduc.org) * Co-creators: Vais