Introduction to Statistics

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July 24-25, 8-11 AM PST

Workshop Description Summer 2023

This workshop covers the fundamentals of statistics, which powers modern day machine learning, deep learning and data science. This course will provide an overview of the key methodologies of statistics, which is also known as the science of learning from data. The course will be introductory and does not require a background in statistics. We will cover basic techniques on how to visualize data, sample and conduct experiments. We then will detail statistical approximations including mean and standard deviation estimates from data, the normal approximation, and central limit theorem, as well as common probability rules and distributions for different types of data. We will demonstrate the important concepts and pitfalls of regression as well as regression error analysis including the bias-variance tradeoff, and how to do inference with confidence intervals and tests of hypotheses. By the end of this workshop, participants will have developed a foundational understanding and hands-on experience with the statistical fundamentals behind Big Data and data science, which will be important for the subsequent machine learning related workshops.

About the Instructor

danielle-maddix

Danielle Maddix Robinson is a Senior Applied Scientist in the Machine Learning Forecasting Group within AWS AI Labs. She graduated with her PhD in Computational and Mathematical Engineering from the Institute of Computational and Mathematical Engineering (ICME) at Stanford University. She was advised by Professor Margot Gerritsen and developed robust numerical methods to remove spurious temporal oscillations in the degenerate Generalized Porous Medium Equation. She is passionate about the underlying numerical analysis, linear algebra and optimization methods behind numerical PDEs and applying these techniques to deep learning. She joined AWS in 2018 shortly after graduating, and has been working on developing statistical and deep learning models for time series forecasting. In this past year, she has been leading the research initiative on developing models for physics-constrained machine learning for scientific computing. In particular, she has researched how to apply ideas from numerical methods, e.g., finite volume schemes, to improve the accuracy of black-box ML models for ODEs and PDEs with applications to epidemiology, aerodynamics, ocean and climate models.

About the Workshop Assistant

eden-luvishis

Eden Luvishis is a Master’s student in the Institute for Computational and Mathematical Engineering (ICME) at Stanford University in the Computational Finance Track. She received her Bachelor’s from Stevens Institute of Technology in Quantitative Finance and has been involved in a number of research and professional experiences. She completed three internships in quantitative trading and research with a focus in signal research and predictor modeling as well as cross-instrument trading. Her reserach has focused on the application of generative AI to financial modeling and hedging problems and she has presented findings at the R/Finance conference. She has a deep appreciation for data science and is excited about discovering its newest applications.

Workshop Materials

Pre-workshop Checklist

  1. Make sure you have the Zoom links to the course. If not please email us (edenl [at] stanford [dot] edu). Zoom Link Here
  2. Familiarize with the schedule posted below.
  3. Join Piazza at this link. Access code: icmestats2023

Schedule and Slides

You can access the lecture slides here: Slides

Session 1: Monday July 24th, 8:00 - 9:20 AM. PT

Session 2: Monday July 24th, 9:40 - 11:00 AM. PT

Solutions for Session 1 and 2: Solutions

Zoom recording day 1: Link

Session 3: Tuesday July 25th, 8:00 - 9:20 AM. PT

Solutions for Session 3: Solutions

Session 4: Tuesday July 25th, 9:40 - 11:00 AM. PT

Solutions for Session 4 & 5: Solutions

Zoom recording Day 2: Link

Please complete this post-course survey to receive your certificate: Post Course Survey

Additional Resources & Slides

Here are some additional resources for various topics: