ОУФ Машинное Обучение в Питоне — различия между версиями
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'''Ассистенты''': [https://canvas.instructure.com/courses/2289452 see Canvas LMS] | '''Ассистенты''': [https://canvas.instructure.com/courses/2289452 see Canvas LMS] | ||
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| + | This course introduces the students to the elements of machine learning, including supervised and unsupervised methods such as linear and logistic regressions, splines, decision trees, support vector machines, bootstrapping, random forests, boosting, regularized methods and much more. The two modules (Sept-Dec, 2020) use Python programming language and popular packages to investigate and visualize datasets and develop machine learning models. | ||
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| + | '''Пререквизиты курса''': at least one semester of calculus on a real line, vector calculus, linear algebra, probability and statistics, computer programming in high level language such as Python or R. | ||
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| + | '''Технические требования''': Laptop, Internet connection, Chrome web browser, Google Drive, Google Colab. | ||
Версия 18:36, 3 сентября 2020
О курсе
Курс читается в 1-2 модулях.
Instructor: Oleg Melnikov
Ассистенты: see Canvas LMS
This course introduces the students to the elements of machine learning, including supervised and unsupervised methods such as linear and logistic regressions, splines, decision trees, support vector machines, bootstrapping, random forests, boosting, regularized methods and much more. The two modules (Sept-Dec, 2020) use Python programming language and popular packages to investigate and visualize datasets and develop machine learning models.
Пререквизиты курса: at least one semester of calculus on a real line, vector calculus, linear algebra, probability and statistics, computer programming in high level language such as Python or R.
Технические требования: Laptop, Internet connection, Chrome web browser, Google Drive, Google Colab.
