ml-algorithms.yaml

# 2-half-days exploring machine learning algorithms.
title: Machine Learning Algorithms
environment: geoml
scripts:
  - mlutils.py  # Copied to all notebook directories.
curriculum:
  1a:
    - Quick course overview
    - Introductions
    - What is machine learning?
    - Linear_regression.ipynb
  1b:
    - Classification_algorithms.ipynb
    - Choosing algorithms  # Conceptual: evaluating performance, model tuning.
    - Check out and feedback
  2a:
    - Check in and review
    - Linear_regression_algorithms.ipynb  # KNN, SVR, Trees and forests.
  2b:
    - Unsupervised_learning_algorithms.ipynb  # Very quick treatment: PCA, KMeans, DBSCAN, manifolds.
    - Neural networks and beyond  # Qualitative, depends on time.
    - Check out and feedback
extras:
  - Binary_classification.ipynb
  - Intro_to_classification.ipynb
  - Intro_to_NumPy_for_ML.ipynb
  - Intro_to_regression.ipynb
  - Loading_messy_data.ipynb
  - Nonlinear_regression_with_Gardner_equation.ipynb
  - Read_and_write_LAS.ipynb
  - Read_SEG-Y_with_segyio.ipynb
  - What_is_gradient_descent.ipynb
references:
  - Dramsch_2020.pdf