Machine Learning, Deep Learning and Unsupervised Learning: Theory and Case Studies with R - учебный курс | ИСТИНА – Интеллектуальная Система Тематического Исследования НАукометрических данных

Автор: Fantazzini Dean
Год создания: 2024
Организация: МГУ имени М.В. Ломоносова
Курс на иностранном языке
Описание: The course aims to provide a comprehensive understanding of advanced machine learning techniques and their practical applications through theoretical learning and real-world case studies. The primary objectives of this course encompass both theoretical foundations and hands-on applications within the domains of Marketing, Energy Economics, and Finance. Students will delve into the fundamental theories of machine learning, deep learning, and unsupervised learning, acquiring a robust comprehension of algorithms, models, and their underlying principles. They will explore the intricacies of neural networks, deep learning architectures, clustering algorithms, and dimensionality reduction techniques, among others. Through practical demonstrations and interactive sessions, students will gain proficiency in implementing these methods using the R programming language. This course uniquely focuses on the application of machine learning methodologies within specific domains, namely Marketing, Energy Economics, and Finance. In the Marketing realm, students will learn to navigate complex marketing datasets, conducting exploratory analyses and predictive analytics to optimize decision-making processes and mitigate data complexity. In Energy Economics, the course will cover electricity demand and temperature forecasting, enabling students to forecast demand patterns crucial for energy planning and resource allocation. Finally, within the Finance domain, students will explore modeling and forecasting techniques to assess the probability of closure of a crypto-exchange, manage credit risk effectively, and detect pump-and-dump schemes within the realm of crypto-assets. Through a blend of theoretical insights and hands-on case studies using R, this course aims to equip participants with the expertise to not only comprehend complex machine learning concepts but also apply them effectively to solve real-world challenges across diverse industries. By the course’s conclusion, students will possess the skills necessary to approach data-driven problems, build predictive models, and extract actionable insights from varied datasets in Marketing, Energy Economics, and Finance using machine learning methodologies.
Добавил в систему: Фантаццини Деан c.

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Machine Learning, Deep Learning and Unsupervised Learning: Theory and Case Studies with Rучебный курс

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