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Workshop 05/26/2021


This is the second of our workshops devoted to linear algebra, which forms
the foundation of many algorithms in data science.
In part I of the series (maybe with a link to the video?)
we introduced vector and matrix algebra, and briefly looked
at the intriguing and ever so useful Singular Value Decomposition (SVD).
In this workshop, we will take a deeper into the SVD. We will explain how
it is derived, how it can be computed, and also how it is used.

Want to learn more about trends like AI, IoT and wearable tech? In one hour, we will cut through the hype by building a “smart” fitness tracker using your own mobile device. We’ll do hands-on exercises:
you’ll acquire data from sensors, design a step counter and train a human activity classifier. You will leave motivated and ready to use machine learning and sensors in your own projects!

In this workshop, Dora will illustrates how natural language processing (NLP) can be used to answer social science questions. The workshop will focus on applying NLP to analyze the content of 15 US history textbooks used in Texas, to analyze the representation of historically marginalized people and groups. The workshop is based on a paper ( that also has an associated toolkit, and it will provide examples of how this toolkit can be used using a Jupyter notebook that will be made available.

(ps Dora was recently in the HAI newsletter:

Event Program

May 26, 2021

*All times are UTC -8

Workshop Instructors

Margot Gerritsen

Executive Director

Laura Lyman

Instructor of Mathematics, Statistics, and Computer Science (MSCS), Macalester College​

Louvere Walker-Hannon

Application Engineering Senior Team Lead, MathWorks

Shruti Karulkar

Quality Engineering Manager, MathWorks

Sarah Mohamed

Senior Software Engineer, MathWorks

Dora Demszky

PhD Candidate, Stanford University

Julia Olivieri

PhD Candidate, Stanford University, ICME


What would we do without Linear Algebra, Part II: Diving Deeper, Singular Value Decomposition

TOPICS: Algorithms , Values

Pocket AI and IoT, or How to be a Data Scientist using Your Mobile Device | Mathworks

TOPICS: Algorithms , Data Generation/Collection , Software Design and Engineering , Values

Graph Theory for Data Science, Part I: What is a graph and What Can We Do With It?

TOPICS: Algorithms , Data Wrangling , Software Design and Engineering

Using Natural Language Processing to Analyze US History Textbooks

TOPICS: Algorithms , Data Science as a Career , Foundations (Mathematics/Statistics) , Software Design and Engineering , Values