| 1 |
Week 1
? The aims of the course.
? Overview of studies exploring the application of machine learning in atmospheric sciences.
Week 2
1. Python for Data Science (3 hours)
? Introduction to Python libraries: NumPy, pandas, Matplotlib, Seaborn
? Basic data manipulation and visualization (Importing, Summarizing and Visualizing Data) ? Exercises
Week 3
2. Introduction to Machine Learning
? 2.1 Statistical Learning (3 hours)
? Supervised and Unsupervised Learning, Reinforcement
? Training and Test Loss, Validation, Tradeoffs in Statistical Learning, Estimating Risk. ? Exercises
Week 4
? 2.2 Statistical Learning (3 hours)
? Modeling Data
? Multivariate Normal Models
? Normal Linear Models
? Bayesian Learning
Week 5
3. Supervised Learning
? 3.1 Regression Models (3 hours)
? Linear regression, polynomial regression ? Evaluation metrics (MAE, MSE, RMSE) ? Exercises
Week 6
? 3.2 Classification Models (3 hours)
? Logistic regression, decision trees, random forests
? Evaluation metrics (accuracy, precision, recall, F1-score)
Week 7
? 3.3 Model Selection and Tuning (3 hours)
? Hyperparameter tuning (Grid Search, Random Search) ? Cross-validation techniques
? Model comparison and selection
Week 8
Assignments
Week 9
4. Unsupervised Learning
? 4.1 Clustering Algorithms (3 hours)
? Empirical Distribution and Density Estimation ? Clustering via Mixture Models
? Clustering via Vector Quantization
? Exercises
Week 10
? 4.2 Clustering Algorithms (3 hours)
? Hierarchical Clustering
? Principal Component Analysis (PCA)
Week 11
5. Decision Trees and Ensemble Methods
? Top-Down Construction of Decision Trees ? Controlling the Tree Shape
? Bootstrap Aggregation
? Random Forests
? Boosting ? Exercising
Week 12
Assignments
Week 13
6. Machine Learning Workflow (3 hours)
? Data collection and preprocessing ? Model selection and evaluation
? Model deployment and monitoring ? Exercising
Week 14
7. Advanced Topics
? 7.1 Advanced Topics (Deep Learning in Climate and Marine Sciences (3 hours)
Week 15
Presenting the projects |