Module #1 Introduction to Climate Prediction Overview of climate prediction, its importance, and the role of machine learning
Module #2 Basics of Machine Learning Introduction to machine learning concepts, supervised and unsupervised learning, and regression vs. classification
Module #3 Climate Data and Sources Overview of climate data sources, types, and formats, including satellite imagery, sensor data, and climate models
Module #4 Data Preprocessing for Climate Data Techniques for preprocessing climate data, including data cleaning, feature scaling, and normalization
Module #5 Introduction to Supervised Learning for Climate Prediction Basic concepts of supervised learning, including regression and classification, and their applications in climate prediction
Module #6 Linear Regression for Climate Prediction Application of linear regression to climate prediction, including simple and multiple linear regression
Module #7 Decision Trees for Climate Prediction Application of decision trees to climate prediction, including advantages and limitations
Module #8 Random Forest for Climate Prediction Application of random forest to climate prediction, including hyperparameter tuning and feature importance
Module #9 Gradient Boosting for Climate Prediction Application of gradient boosting to climate prediction, including XGBoost and LightGBM
Module #10 Introduction to Deep Learning for Climate Prediction Basic concepts of deep learning, including neural networks and convolutional neural networks
Module #11 Convolutional Neural Networks (CNNs) for Climate Prediction Application of CNNs to climate prediction, including image-based climate data and spatial-temporal analyzes
Module #12 Recurrent Neural Networks (RNNs) for Climate Prediction Application of RNNs to climate prediction, including time series forecasting and sequence analysis
Module #13 Long Short-Term Memory (LSTM) Networks for Climate Prediction Application of LSTMs to climate prediction, including handling non-stationarity and long-term dependencies
Module #14 Unsupervised Learning for Climate Data Analysis Introduction to unsupervised learning, including clustering, dimensionality reduction, and anomaly detection
Module #15 K-Means Clustering for Climate Data Analysis Application of k-means clustering to climate data, including identifying patterns and grouping similar observations
Module #16 Principal Component Analysis (PCA) for Climate Data Analysis Application of PCA to climate data, including dimensionality reduction and feature extraction
Module #17 Anomaly Detection for Climate Data Analysis Introduction to anomaly detection, including one-class SVM, Local Outlier Factor (LOF), and Isolation Forest
Module #18 Ensemble Methods for Climate Prediction Introduction to ensemble methods, including bagging, boosting, and stacking, and their applications in climate prediction
Module #19 Hyperparameter Tuning for Climate Prediction Models Techniques for hyperparameter tuning, including grid search, random search, and Bayesian optimization
Module #20 Model Evaluation and Selection for Climate Prediction Metrics and techniques for evaluating and selecting machine learning models for climate prediction
Module #21 Handling Uncertainty in Climate Prediction Introduction to uncertainty quantification, including Bayesian neural networks and ensemble methods
Module #22 Explainability and Interpretability in Climate Prediction Introduction to explainability and interpretability techniques, including feature importance, partial dependence plots, and SHAP values
Module #23 Case Studies in Climate Prediction using Machine Learning Real-world examples of machine learning applications in climate prediction, including temperature forecasting, precipitation prediction, and climate change attribution
Module #24 Course Wrap-Up & Conclusion Planning next steps in Machine Learning Models for Climate Prediction career
