Machine Learning 1 DSBA 2025/2026

Course Syllabus

DSBA Machine Learning 1, ICEF Machine Learning 2025-2026. This syllabus is shared by 2 programs (differences specified where necessary):

• DSBA (ПАД ФКН): Syllabus
• ICEF (МИЭФ): Syllabus

Teachers and Assistants

Role	DSBA 231	DSBA 232	DSBA 233	DSBA 234	ICEF
Lecturers	Alexey Boldyrev, Maksim Karpov, Lecturers' TA Georgiy Solovev
Seminarists	Kirill Bykov	Sara Ali	Aleksandr Khizhik	Vsevolod Ovchinnikov	Majid Sohrabi
Teaching Assistants	Nikolay Dvoryanchikov	Adamey Laipanov	Stanislav Ryazanov	Maria Sudakova	Isa Gadaev

Useful links

1. Moodle LMS (a.k.a. Smart LMS): for posting weekly material, grading quizzes, tests, and HW, etc.
2. Google Drive: for release of seminar Colab notebooks, lecture presentation slides, Starter Colab files for Kaggle competitions.
3. Google Colaboratory: for individual manual-graded assignments, group Kaggle assignments and reproducible seminar’s notebooks.

• We require the use of LaTeX and Markdown syntax for all write ups.

1. Kaggle.com: for data science competitions in teams of 1-3 students.

• Ensure that your name & surname match exactly to those in Moodle LMS or we can lose you in grade matching. Update the profile with your presentable photo to help us authenticate you and credit your effort.

1. Telegram Сhat (the link is in Smart LMS): for the course announcements (HW, timetable changes, etc.).

Course Description

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.

• The first two modules (Sep-Dec`25) DSBA and ICEF students apply Python programming language and popular packages to investigate/visualize datasets and develop machine learning models that solve theoretical and data-driven problems.

1. The course aims to help the students to develop an understanding of learning from data, to familiarize them with a wide variety of algorithmic and model based methods to extract information from data, teach to apply and evaluate suitable methods to various datasets by model selection and predictive performance evaluation.
2. DSBA and ICEF students: the course is designed to prepare DSBA/ICEF students for the upcoming University of London (UoL) examination.

Grading System

Relevant grading formulas are presented on the official course home page (DSBA Machine Learning 1, ICEF Machine Learning). Moodle’s gradebook shows your up to date performance, including your current constituent and aggregate grades.

• DSBA

First Semester Grade = 0.15 * Final Test + 0.15 * Hackathon (Exam) + 0.25 * Kaggle competitions + 0.1 * Home assignments + 0.15 * Midterm Test + 0.2 * Quizzes We use natural grade aggregation in LMS. Rounding to the nearest integer is used to report 0-10 scale grades to HSE.

• ICEF

0.1 * Midterm Test Semester 1 + 0.1 * Final Test Semester 1 + 0.1 * Midterm Test Semester 2 + 0.075 * Hackathon Semester 1 + 0.075 * Hackathon Semester 2 (Exam) + 0.125 * Kaggle competitions Semester 1 + 0.125 * Kaggle competitions Semester 2 + 0.05 * Home assignments Semester 1 (Stack, Colab notebooks) + 0.05 * Home Assignments Semester 2 + 0.1 * Quizzes Semester 1 + 0.1 * Quizzes Semester 2

• ICEF rules:

1) Final grade is initially calculated out of 100 points and then converted to the 10-points scale according to the following preliminary scale: 100-point scale =>10-point scale (5-point scale): 0-9,99 => 1 (fail); 10-14,99 => 2 (fail); 15-24,99 => 3 (fail); 25-29,99 => 4 (satisfactory); 30-39,99 => 5 (satisfactory); 40-49,99=> 6 (good); 50-59,99 => 7 (good); 60-69,99 => 8 (excellent); 70-84,99 => 9 (excellent); 85-100 => 10 (excellent). The grades may be adjusted within 10 points of 100-point scale uniformly for all students by the decision of the lecturer. The scale is finalized after the results in 100-point scale are obtained.

2) In case of missing a midterm with weight less than 30% for a valid reason the student may submit a motivated application to the Head of BSc academic programme to authorize the use of compensatory coefficient for the final grade (1+0.5a) where a = the weight of the missed midterm. The application is to be submitted within 7 days after the date of the missed midterm with the Head of BSc academic programme making a decision regarding the validity of the reason for absence. In case there is more than one such midterm, the application is only submitted once and missing other midterms is not compensated.

3) In order to get a passing grade for the course, the student must sit the exam in the form of Hackathon.

Homework (HW) Assignment

• HW is released via Moodle/Smart LMS and is also announced in the Telegram Channel.
• Individual: These are individualized (not in groups!) assignments of two kinds
  • Auto-graded assignments (thanks to STACK plugin with Maxima CAS backend). Some written responses will be selectively hand-graded by TAs.
  • Quick Maxima syntax is provided below and in each assignment.

• Manual-graded assignments include analysis of datasets, analytical and conceptual problems, and programming assignments.
  • All text explanations must be written in Markdown cells.
  • Graders leave feedback in Smart LMS and execute Jupyter notebook to reproduce your results.
• Group: Students are self-assigned into groups to compete on Kaggle.com. Teamwork is evaluated by instructors. Team’s grade is awarded to everyone on a team.

Midterm and Final Test

1. We will have a cumulative in-class midterm test/exam in the middle and at the end of the semester (during the HSE examination sessions). Do not book travel tickets that conflict with test dates.
2. Moodle/Smart LMS based in Safe Exam Browser.
3. Tests and exams are individual, i.e. no collaboration. Generative models, web searching, lecture and/or seminar materials, and textbooks are not allowed.
4. Test questions are drawn from quiz banks, not HW. HW deepens your understanding, but a test measures it.
5. There is a free navigation between questions, i.e. you can move back or forward the test questions.
6. Results will be announced after the midterm test/exam within 5 working days.

University of London (UoL), Course ST3189 (ML)

1. Coursework Project in Python (or R) programming language is for DSBA/ICEF students only and is administered by LSE/UoL. It is released around January and is due around April 1. Although students are given a 3-4 months window, this exercise is meant to be completed in a few days. Typically, students work on it in Feb/Mar. More details on the UoL site.

~5 minute Quizzes during seminar in LMS

1. Only students present in the classroom during the seminar are allowed to take the quiz. At the beginning of the seminar the seminar assistant will remove absent student(s) from the group list in moodle settings for the current quiz.
2. Quizzes are conducted in Safe Exam Browser.
3. Quizzes are based on lectures, seminars, textbooks, and other material delivered via our course.
4. Quizzes individualized (shuffled and sampled from question banks) for each student. Most questions are auto-generated.
5. All choice questions are multiple-choice (regardless of singular/plural formulation). Incorrect answers lower your score to prevent guessing.
6. Numeric answers are typically accepted with 0.01=1% of error, i.e. round to at least 4 decimal places, if needed. Please do not round any intermediate calculations. It’s best to use as many decimals as fits in the answer box.
7. The quiz answers are released after all groups write the quiz .
8. We always use natural logarithm (inverse of exp()) in this course.

Kaggle Competitions and Hackathon

1. The assignment is conducted in the form of a kaggle competition in teams of 1-3 students.
2. Leader Board (LB) position below or equal baseline (BL) results in 0 points in LB category.
3. Above baseline is split into 4 quartiles: 1Q (top 25%), 2Q (top 50%), 3Q (top 75%), 4Q (bottom 25%).

Deadline Extensions and Makeup

1. Only valid verifiable excuses are accepted for 1-2 day extensions.
   • DSBA and ICEF students: submit your doctor's note via student services/study office.
2. If you miss a deadline (with a valid/verifiable excuse), contact instructors ASAP to arrange a new deadline.
3. Submissions are not allowed after the solutions have been released.
4. Any test/exam retakes will be rescheduled as per university policy (see also dedicated rubric)
5. Note: accommodating exceptions is difficult and time consuming. Typically, a verifiable medical emergency is a valid reason, but travel and conferences are not. It is the student's responsibility to start their work early, so as to hedge against any unforeseeable life event.