100728 - CGPR - Fundamentals of Machine Learning for Geotechnical Practice

Overview

Over the last 15 years, the use of machine learning (ML) has dramatically increased in many fields of engineering; geotechnical engineering is no exception. With ML utilization expected to continue to increase, this course seeks to provide a guided tour of ML techniques and their strengths and limitations. It will focus on best-practices for developing and evaluating ML models and will cover aspects associated with data management & preparation, model selection, model training, and model evaluation.

The course is divided into two days. Day 1 will include a high-level presentation of ML techniques with an emphasis on best-practices and approaches for users to quantitatively evaluate ML model performance. Day 2 will focus on hands-on activities and is geared toward those interested in the ML model development process. Day 2 activities include data preparation and the development of regression & classification models using ML in a Python programming environment.

Location Information

Virginia Tech Campus
Blacksburg, Virginia 24061

Agenda/Schedule

DAY 1
08:30 - 09:00 - Registration (Tea/Coffee)
09:00 - 10:30 - Introduction to ML
10:30 - 10:45 - Morning Break (Tea/Coffee)
10:45 - 12:00 - Model Development Basics
12:00 - 13:00 - Lunch Break (Catered)
13:00 - 14:00 - Data Fundamentals
14:00 - 15:00 - Simple ML Models
15:00 - 15:15 - Afternoon Break (Tea/Coffee)
15:15 - 16:45 - Advanced ML Models
16:45 - 17:00 - Wrap Up

DAY 2
08:30 - 09:00 - Registration (Tea/Coffee)
09:00 - 10:30 - Hands-On Data Wrangling
10:30 - 10:45 - Morning Break (Tea/Coffee)
10:45 - 12:00 - Hands-On Regression Models
12:00 - 13:00 - Lunch Break (Catered)
13:00 - 14:00 - Hands-On Model Tuning
14:00 - 15:00 - Hands-On Classification Models
15:00 - 15:15 - Afternoon Break (Tea/Coffee)
15:15 - 16:40 - Beyond Supervised ML
16:40 - 17:00 - Wrap Up

Speaker(s)/Instructor(s)

Dr. Joseph P. Vantassel earned his B.S. in Civil Engineering in 2016 from Rensselaer Polytechnic Institute (RPI) in Troy, NY, where he focused his studies on the areas of geotechnical engineering, geology, and structural engineering. During his undergraduate studies, Dr. Vantassel held a number of professional appointments including: construction inspector at the Massachusetts’s Department of Transportation, undergraduate researcher at the University of Minnesota’s Large-Scale Structure’s Lab, project engineer at Schnabel Foundation Company, and undergraduate researcher at Rensselaer’s Geotechnical Centrifuge. For his graduate studies, Dr. Vantassel attended The University of Texas at Austin, earning his M.S. in May of 2018 and Ph.D. in December of 2021, in Civil Engineering. His graduate studies focused heavily on the intersection of geotechnical engineering, geophysics, and computer science, in particular the areas of signal processing and the solution of inverse problems. After earning his PhD, Dr. Vantassel worked as a Research Associate in the Data Intensive Computing Group at the Texas Advanced Computing Center (TACC), the largest academic computing facility in the United States, until fall 2023. His work at TACC focused primarily on partnering with users from academia, industry, and government to best utilize the computational power available at TACC with an emphasis on machine-learning and big-data workflows. Dr. Vantassel is currently an Assistant Professor of Geotechnical Engineering in the Department of Civil and Environmental Engineering at Virginia Polytechnic Institute and State University (Virginia Tech). He leads a group focused on advancing subsurface imaging toward more-robust and uncertainty-aware solutions through the intersection of field experiments, numerical simulation, artificial-intelligence, and high-performance computing.

Enroll Now - Select a section to enroll in

100728 - 566802

2024

Available

CGPR - Fundamentals of Machine Learning for Geotechnical Practice

Discussion

W, Th

8:30AM to 5:00PM

Aug 07, 2024 to Aug 08, 2024

Schedule and Location

View Details

13.0

Virginia Tech - Blacksburg (Main Campus)

Fees starting at

$300.00

1.3 CEUs

Section Notes

For the most up-to-date information related to this program, please visit: https://register.cpe.vt.edu/search/publicCourseSearchDetails.do?method=load&courseId=7040624

Refund and Cancellation Policy:
Refund requests must be received 14 calendar days prior to the program start date. A $30 administrative fee will be deducted from all refunds. Requests should be sent by email or by initiating a drop request through the student portal in our online registration system. As an alternative to a refund, you may send a substitute at no additional cost. Please contact us at 540-231-5182 or e-mail cpeinfo@vt.edu to request a substitution. Please note: refunds will not be issued for no-shows or for cancellations received on or after the program start date. In the unlikely event that this program is cancelled or postponed due to insufficient enrollments or unforeseen circumstances, the university will fully refund registration fees but cannot be held responsible for any other expenses, including cancellation or change charges assessed by airlines, hotels, travel agencies, or other organizations.

Anyone requesting special requirements for this program should contact cpeinfo@vt.edu.