Machine learning 1 DSBA 2023/2024
Course description
This course introduces the students to the elements of machine learning (ML), including supervised and unsupervised methods such as linear and logistic regressions, splines, decision trees, support vector machines, bootstrapping, random forests, boosting, regularized methods, etc. The course covers classic ML. The weekly or biweekly team-based Kaggle competitions are released in Python programming language. Other assignments (quizzes and theoretical derivations) are highly individualized and autograded with tools in Moodle LMS. Participation at lectures, seminars and a class forum is assessed and graded. Pre-requisites: calculus 1, vector calculus, linear algebra, probability/statistics, computer programming in a high level language such as Python. This course is separated into two differing elective tracks, namely, Introduction to Statistical Learning (ISL) and Fundamentals of Statistical Learning (FSL). ISL offers a more practical (hands-on) approach, while FSL is more focused on the theoretical aspect of the course' materials. The introductory experience in machine learning for FSL track is required.
General information
Lectures
| ISL | Alexey Boldyrev |
| FSL | Maksim Karpov |
Seminars
| Group | ISL group 1 | ISL group 2 | ISL group 3 | FSL |
|---|---|---|---|---|
| Professor | Tatiana Yazykova | Kirill Bykov | Maksim Karpov | Tigran Ramazyan |
| Seminar date/time (room) | Sat 14:40 (D207) | Thu 09:30(D207) | TBA | Tue 13:00(R608) |
Teaching assistants(Both tracks)
- Tim Sluev
- Isa Gadaev
- Adam Alkhanashvili
- Daria Lapko
- Grigorii Chebotarev
- Petr Belonovskii
- Elizaveta Kurlovich
- Artem Astashkin
Grading formula
2023/2024 2nd module
0.3 * Home assignments + 0.2 * Quizzes + 0.2 * Test + 0.1 * Participation + 0.2 * Exam
Moodle’s gradebook shows your up to date performance, including your current constituent and aggregate grades. If you suspect an error in grade calculation, please let us know ASAP. We use natural grade aggregation in LMS. Rounding (to the nearest integer) is used to report 0-10 scale grades to HSE. There are no blocking grading components.
Important links
- ISL course page on the HSE website
- ISL course page on the HSE website
- Syllabus
- Moodle/SmartLMS page
- HSE MLP Discourse (used to communicate with the Teaching Team)
