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	-	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		This course is designed to provide students with a comprehensive understanding of machine learning concepts and algorithms, with a specific focus on atmospheric science applications. Here’s a breakdown of the course: Fundamentals of Machine Learning: The course starts by introducing the core concepts of machine learning, covering the foundational algorithms that form the backbone of data-driven decision-making and predictions. Students will learn how these algorithms are applied to real-world problems. Python Programming and Data Analysis: Students will gain practical experience in Python programming, learning how to manipulate data, build models, and perform data analysis. Special attention will be given to working with atmospheric datasets, helping students develop the necessary skills to handle complex, real-world data. Machine Learning Applications in Atmospheric Sciences: The course will delve into the practical applications of machine learning algorithms in atmospheric sciences. Topics will include weather forecasting, climate modeling, and environmental monitoring. Students will explore how machine learning can enhance predictive accuracy and provide valuable insights in these fields. Research and Professional Development: In addition to technical skills, the course will prepare students for further academic research or professional roles in machine learning and atmospheric sciences. Current trends, challenges, and advancements in the field will be discussed, ensuring students are aware of the latest developments and are equipped to contribute to the evolving landscape of machine learning in atmospheric research. By the end of the course, students will have a strong foundation in both machine learning techniques and their practical application in atmospheric science, setting them up for success in both research and industry roles.
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		1. Data Science and Machine Learning, Mathematical and Statistical Methods. Drik P. Kroese, Zdravko I. Botev, Thomas Taimar, and Radislav Vaisman. CRC Press, Taylor & Francis Group. 2. Introduction to Machine Learning with Python. A. C. Muller and S. Guido. 3. The Elements of Statistical Learning, Data Mining, Inference, and Prediction. Trevor Hastie Robert Tibshirani Jerome Friedman.
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