Workshop 09/29/2021

In this third workshop in linear algebra, we will investigate the link between Principal Component Analysis and the Singular Value Decomposition.

Along the way, we are introduced to several linear algebra concepts including linear regression, eigenvalues and eigenvectors and conditioning of a system. We will use shared python scripts and several examples to demonstrate
the ideas discussed.

This workshop builds on the previous 2 workshops in linear algebra (PLEASE INCLUDE THE LINKS), and we will assume that the linear algebra concepts introduced in those workshops are familiar to the audience. They include:
vector algebra (including inner products, angle between vectors), matrix-vector multiplications, matrix-matrix multiplications, matrix-vectors solves, singularity,
and singular values.
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Want to learn more about trends like AI, IoT and wearable tech? In this workshop, 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!
The goal for this session is twofold: show our participants how easy it is to get started with Machine Learning and wearables and set them up for success to take on challenging projects in this domain. This workshop is highly interactive with hands-on exercises in which the participants will create a “smart” wearable device and test it out. We aim to demonstrate how powerful applications can be built when we combine machine learning, sensor analytics, and IoT. Participants will use the free MATLAB Mobile App to access built-in sensors and analyze data on their mobile devices.
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How can sharing stories help us as a community? How do we learn how to find a story from the events of someone else’s life or our own? How can this relate to our own tendency as data-scientists to connect the dots, to find meaning through patterns? Join us in this WiDS workshop on telling and sharing stories where we will address these questions and learn how our stories are important in shaping the community we want to see in Data Science.
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At its root, Bayesian Machine Learning builds statistical models using Bayes’ Theorem. While these methodologies are less explored than their frequentist counterparts, BML finds widespread applications in domains where data is limited and explainability of a model is paramount (i.e., domains where deep learning fails!). In Walmart, BML is widely used in Healthcare and Shrink studies. With growing advancement in GPU availability and open-sourced sampling algorithms, BML is seeing traction like never before. However, Bayesian isn’t for the faint of heart! We are here to make it a tad simpler to start with.

In this workshop, you will learn about the core concepts of BML – how it is different from the frequentist approaches, building blocks of Bayesian inference and what known ML techniques look like in a bayesian set-up. You will also learn how to use various sampling techniques for bayesian inference and why we need such techniques in the first place. The workshop will also provide links and materials to continue your Bayesian journey afterwards.

This workshop is meant as an introduction to select BML modules – we strongly recommend you to continue exploring the world of bayesian once you have taken this first step. Happy learning!