Dersin Bilgileri

Dersin Adı	Türkçe	Special Top.in Earth Sys.Sci (Applied Machine Learning in Atmospheric Sciences with Python).
	İngilizce	Special Top.in Earth Sys.Sci (Applied Machine Learning in Atmospheric Sciences with Python).
Dersin Kodu	YSB 579E	Kredi	Ders (saat/hafta)	Uygulama (saat/hafta)	Labratuvar (saat/hafta)
Dönem	1	3	2	1	-
Dersin Dili		İngilizce
Dersin Koordinatörü		Yusuf Aydın
Dersin Amaçları		? Understanding the principal conceptions of machine learning and its algorithms. ? Prepare students for python programming and data analysis, model building, and evaluation in the context of atmospheric datasets. ? Explore various machine learning algorithms and their applications in tasks such as weather forecasting, climate modeling, and environmental monitoring. ? Prepare students for further research or professional work by introducing them to current trends and challenges in the field of atmospheric sciences and machine learning.
Dersin Tanımı		Course Duration: 16 weeks (3 hours for each week, in total 48 hours) Course Outline 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 6. 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 Paper – Muhammed Denizoglu and Ismail 7. Machine Learning Workflow (3 hours) ? Data collection and preprocessing ? Model selection and evaluation ? Model deployment and monitoring ? Exercising Week 14 9. Advanced Topics ? 9.1 Advanced Topics (Deep Learning in Climate and Marine Sciences (3 hours) Week 15 Presenting the projects Week 16 Presenting the projects
Dersin Çıktıları		Understanding the principal conceptions of machine learning and its application in atmospheric sciences with python.
Önkoşullar		Basic experiences in programming and statistics.
Gereken Olanaklar
Diğer
Ders Kitabı
Diğer Referanslar

Dersler

Yardım

Hakkında