Machine Learning 1 DSBA 2025/2026 modules 3-4

Course Syllabus

DSBA Machine Learning 1, modules 3-4 2025/2026.

• DSBA Syllabus

Teachers and Assistants

Role	DSBA 241	DSBA 242	DSBA 243	DSBA 244	DSBA 245	DSBA 246
Lecturers	Alexey Boldyrev, Maksim Karpov, Lecturers' TA Irina Milova
Seminarists	Sara Ali	Sara Ali	Majid Sohrabi	Vsevolod Ovchinnikov	Vsevolod Ovchinnikov	Kirill Bykov
Teaching Assistants	Artur Karapetyan	Anna Schukina	Ivan Miniaitsev	Polina Doronicheva	Kirill Zykov	Bogdan Uvarov

Useful links

1. 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.

• Students apply Python programming language and popular packages to investigate/visualize datasets and develop machine learning models that solve theoretical and data-driven problems.

• 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.

• The course is designed to prepare students for the upcoming University of London (UoL) examination.

Grading System

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.

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.
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.