10f Machine Learning: Cross Validation Considerations

Machine Learning Graduate Course, Professor Michael J. Pyrcz

Lecture Summary:

Lecture on model cross validation, including workflows and philosophy.

Here is a list of all my predictive machine learning lectures:

0. Machine Learning Concepts - https://youtu.be/zOUM_AnI1DQ?si=kYrnHLkAZ2_pM0CS

1. Linear Regression - https://youtu.be/-to3JXiae9Y?si=IOFISDhGPABjYDJG

2. Ridge Regression - https://youtu.be/-to3JXiae9Y?si=yKR7gBY8S-knedZC

3. LASSO Regression - https://youtu.be/cVFYhlCCI_8?si=EA6xyIZf6zpQq1Ow

4. Optimization Basics - https://youtu.be/cVFYhlCCI_8?si=wGwqfhMi4SFFx3t6

5. Polynomial Regression - https://youtu.be/z19Hs2HfO88?si=Yrlql4NxBhLvu8Xq

6. Training and Testing - https://youtu.be/owOSiKT3K8E?si=8LcwFHZ6arwjh6Di

7. Model Goodness Metrics - https://youtu.be/g38sEpFOX-0?si=mhGYZe-W7z3UwLGm

8. Model Cross Validation - https://youtu.be/FiX8IWPhTcg?si=p-I7wpb8yW-PC1FE

9. K-nearest Neighbours - https://youtu.be/lzmeChSYvv8?si=hCJJ7Q3H8OG-5KNu

10. Computational Complexity - https://youtu.be/SwyNEnHZxDw?si=Hm0ZIPbsFJXUfOmu

11. K-nearest Neighbours Considerations - https://youtu.be/SwyNEnHZxDw?si=Hm0ZIPbsFJXUfOmu

12. Bayesian Linear Regression - https://youtu.be/LzZ5b3wdZQk?si=OIZOOuaw-ONZXtih

13. Markov Chain Monte Carlo - https://youtu.be/7QX-yVboLhk?si=E-Lx8cdk9K1Yoz62

14. Bayesian Linear Regression Example - https://www.youtube.com/watch?v=JG69fxKzwt8&list=PLG19vXLQHvSC2ZKFIkgVpI9fCjkN38kwf&index=35

15. Naive Bayes - https://youtu.be/BDvyLrH3cLI?si=kssB6MLNK9bovIHX

16. Decision Tree - https://youtu.be/JUGo1Pu3QT4?si=S5oRcyNdh1sx2wve

17. Random Forest - https://youtu.be/m5_wk310fho?si=S7yr2ogePSmC-miJ

18. Gradient Boosting - https://youtu.be/___T8_ixIwc?si=Ozk3vKUpAC1ej7u0

19. Support Vector Machines - https://youtu.be/UpN6TLMJiGg?si=hrQxhirVK0lpgpi-

Note, I have excluded my deep learning predictive modeling lectures for brevity.

Free, Online Course e-book Chapter:

Cross Validation - https://geostatsguy.github.io/MachineLearningDemos_Book/MachineLearning_training_tuning.html#train-and-test-hold-out-cross-validation

Theory and well-documented workflows linked to the lectures and interactive Python dashboards.

Course Summary:

Welcome to Subsurface Machine Learning, a graduate-level course I teach at The University of Texas at Austin. While the course is officially titled “Subsurface Machine Learning” to highlight its focus on applications in subsurface modeling, the material is broadly applicable and designed to equip you with foundational and advanced machine learning skills relevant across diverse fields.

The course begins with core concepts in spatial and subsurface modeling, grounded in geoscience and engineering principles, and builds a solid foundation in probability, statistics, and feature engineering and selection. From there, we explore inferential and predictive machine learning techniques, advancing all the way through to cutting-edge deep learning methods. Throughout the course, I strive to make complex topics accessible, providing a clear pathway to mastering machine learning for real-world challenges and empowering you to confidently navigate and harness the power of the ongoing digital revolution.

Course Resources:

Course e-book: https://geostatsguy.github.io/MachineLearningDemos_Book

Course YouTube Playlist: https://www.youtube.com/playlist?list=PLG19vXLQHvSC2ZKFIkgVpI9fCjkN38kwf

My Shared Educational Content:

YouTube: https://www.youtube.com/GeostatsGuyLectures

GitHub: https://github.com/GeostatsGuy

Twitter: https://x.com/GeostatsGuy

I share all of my university educational content to support students and working professionals interested to learn data analytics, geostatistics, and machine learning.

More About the Author:

Find out more about my graduate students, my research consortium, etc. I am happy to discuss research collaboration and short courses.

I hope that you find my educational course content helpful on your data science journey,

Professor Michael J. Pyrcz

Cockrell School of Engineering

Jackson School of Geosciences

The University of Texas at Austin

#dataanalytics #datascience #geostatistics #machinelearning