MachineLearning1 DSBA2020 — различия между версиями
NATab (обсуждение | вклад) м |
NATab (обсуждение | вклад) м |
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== About the course == | == About the course == | ||
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''' All course materials including your results are available at the CANVAS platform ''' | ''' All course materials including your results are available at the CANVAS platform ''' | ||
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| + | ''' The full course syllabus can be found here: [https://www.hse.ru/ba/data/courses/339555328.html official HSE course page]. ''' | ||
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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. The next two modules (Jan - May, 2021) use R programming language to prepare students for the exam from the University of London (UoL) and London School of Economics (LSE), which will count towards the UoL degree of DBSA and ICEF students. Pre-requisites: 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. | ||
==Grading== | ==Grading== | ||
Interim assessment (1 module) = 0.35*Homework + 0.1*Quizzes + 0.05*Participation + 0.5*Exam <br> | Interim assessment (1 module) = 0.35*Homework + 0.1*Quizzes + 0.05*Participation + 0.5*Exam <br> | ||
Interim assessment (4 module) = 0.35*Homework + 0.1*Quizzes + 0.05*Participation + 0.5*(Module1 + T2 + T3+ 2*UOL) | Interim assessment (4 module) = 0.35*Homework + 0.1*Quizzes + 0.05*Participation + 0.5*(Module1 + T2 + T3+ 2*UOL) | ||
Текущая версия на 13:39, 9 апреля 2021
Lecturers and Teachers
| Group | 181 | 182 |
|---|---|---|
| Lecturer | Oleg Melnikov | |
| Teachers | Boris Tseytlin | Stepan Zimin |
About the course
All course materials including your results are available at the CANVAS platform
The full course syllabus can be found here: official HSE course page.
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. The next two modules (Jan - May, 2021) use R programming language to prepare students for the exam from the University of London (UoL) and London School of Economics (LSE), which will count towards the UoL degree of DBSA and ICEF students. Pre-requisites: 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.
Grading
Interim assessment (1 module) = 0.35*Homework + 0.1*Quizzes + 0.05*Participation + 0.5*Exam
Interim assessment (4 module) = 0.35*Homework + 0.1*Quizzes + 0.05*Participation + 0.5*(Module1 + T2 + T3+ 2*UOL)
