Machine Learning for Environmental Sciences Course Overview

I gained hands-on experience and theoretical knowledge across a broad range of topics regarding machine learning for environmental sciences. Below is a detailed week-by-week summary of the course content and the practical exercises I completed.

Week 1: Introduction to Machine Learning

  • Lecture: Definition and history of machine learning.
  • Exercise: Python programming basics and analyzing global warming data (Exercise01.ipynb).

Week 2: Foundations of Machine Learning

  • Lecture: Core principles and foundational concepts.
  • Exercise: Continued work on Python and global warming data analysis (Exercise01.ipynb).

Week 3: Regularization and Hyperparameters

  • Lecture: Techniques such as Ridge and LASSO for regularization, and understanding hyperparameters.
  • Exercise: Exploring the curse of dimensionality (Exercise02.ipynb).

Week 4: Ensemble Methods

  • Lecture: Ensemble regression and classification methods, including Random Forests.
  • Exercise: Implementing random forest regression (Exercise03.ipynb).

Week 5: Explainable AI

  • Lecture: Introduction to SHAP values and feature importances for explainable AI.
  • Exercise: Explaining data-driven models (Exercise04.ipynb).

Week 6: Deep Learning

  • Lecture: Fundamentals of deep learning and convolutional neural networks (CNNs).
  • Exercise: Tuning neural networks for improved performance (Exercise05.ipynb).

Week 7: Recurrent Neural Networks (RNNs)

  • Lecture: Understanding RNNs and Long Short-Term Memory (LSTM) networks.
  • Exercise: Time series forecasting using RNNs (Exercise06.ipynb).

Week 8: Gaussian Process Regression

  • Lecture: Techniques for Gaussian process regression.
  • Exercise: Climate model emulation (Exercise07.ipynb).

Week 9: Unsupervised Learning (I)

  • Lecture: Principal Component Analysis (PCA) and k-means clustering.
  • Exercise: Dimension reduction techniques (Exercise08.ipynb).

Week 10: Unsupervised Learning (II)

  • Lecture: Self-organizing maps and Varimax rotation.
  • Exercise: Analyzing weather maps (Exercise09.ipynb).

Week 11: Causal Discovery

  • Lecture: Techniques for discovering causal relationships.
  • Exercise: From correlation to causation (Exercise10.ipynb).

Week 12: Transformer Architectures

  • Lecture: Introduction to transformer architectures and attention mechanisms.
  • Exercise: Exploring transformer models (Exercise11.ipynb).

Week 13: Anomaly Detection

  • Lecture: Methods for detecting anomalies in data.
  • Recap: Reviewing key concepts and practical applications.

Week 14: Self-Supervised Learning (Optional)

  • Lecture: Introduction to self-supervised learning techniques.
  • Recap: Final review and consolidation of learning.