MachineLearning1 DSBA2020

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)