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Short Course: Machine Learning Methods in Science

Price: 750.00
Will you be attending online or in-person

The short course Machine Learning Methods in Science consists of lectures that explain the high-level concepts underlying fully connected neural networks, convolutional neural networks (CNN), clustering methods, Recurrent Neural Networks, and ChatGPT. It is intended for practicing scientists and engineers in geosciences and engineering who have little knowledge of ML methods but desire a hands-on introduction to Machine Learning (ML) methods and their applications to scientific problems. The goal is to learn the basics of some ML methods and their implementation on free software that can be used for solving real-world problems.

Computer exercises consist of cluster identification of geochemical signatures for rock formations associated with hominin fossils near Lake Turkana, CNN identification of bird types from photographs, CNN detection of cracks along a fractured dam face from drone photos, and discrimination of destructive weeds from plants in drone photos near Kennecott mine.












The course will be delivered on April 4th at the EGI offices (8:30 AM to 4:30 PM MST). For in-person attendees, a laptop with MATLAB installed is preferred or a limited number of workstations with MATLAB will be available at EGI. The topics are listed below. Sessions will be recorded and based on Schuster’s recent book: Machine Learning Methods in Geoscience (library.seg.org/doi/10.1190/1.9781560804048).

The Book: Machine Learning Methods in Geoscience is included with course registration. The digital version is provided for registrants taking the course online.

Course itinerary:

Unsupervised Machine Learning

  • Overview of Course and Machine Learning Methods: 8:30-9:20
  • Unsupervised Clustering: K-Means Clustering: 9:20-9:40

Colab Lab: K-Means Clustering of Yellowstone Old Faithful Earthquake Events: 9:40-10:00

  • Unsupervised Clustering: DBSCAN & PCA: 10:10-10:40

Colab Lab: DBSCAN China Earthquakes: 10:40-11:00

  • Supervised Clustering: Gaussian Discrimination Analysis: 11:50-12:15

MATLAB: Lake Turkana-Africa Geochemical Data and Rock Formation Identification: 12:15-12:30

MATLAB: Distinguishing Weeds from Plants from Multispectral Photos: 12:15-12:30 (Optional)

Break for lunch

Supervised Machine Learning

  • Supervised Machine Learning: Fully Connected Neural Networks: 1:30-2:20
  • Supervised Machine Learning: Convolutional Neural Networks: 2:30-3:20

Colab Labs: Crack Detection in Dams from UAV Photos; Bird

Type Identification from Photos: 3:20-4:00 (Optional)

  • Supervised Machine Learning: Quick Intro to Transformers, LLMs and ChatGPT: 3:30-4:30

30 in-person and 30 online spots available