Бакалавриат
2026/2027
Машинное обучение 1
Статус:
Курс обязательный (Прикладной анализ данных)
Где читается:
Факультет компьютерных наук
Когда читается:
2-й курс, 3, 4 модуль
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
4
Контактные часы:
68
Course Syllabus
Abstract
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, such as pandas, scikit-learn to investigate and visualize datasets and develop machine learning models that solve theoretical and data-driven problems. 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.
Learning Objectives
- The course aims to help students develop an understanding of the process to learn from data, 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.
Expected Learning Outcomes
- Build features suitable for the selected machine learning models
- Evaluate performance of the models
- Tune models to improve prediction and classification performance of the models
- Construct machine learning models on the proposed data sets in Python
- Build and interpret the data visualizations in Python
Course Contents
- Math Essentials. Intro to Python in Google Colab
- Intro to Statistical learning
- Linear Regression (SLR) and K-Nearest Neighbors (KNN)
- Classification with Logistic Regression, LDA, QDA, KNN
- Resampling methods. CV, Bootstrap
- Linear model selection & regularization
- Non-linear regression
- Decision Trees, Bagging, Random Forest, Boosting
- Support Vector Machines/Classifiers
- Clustering methods. PCA, k-Means, Hierarchical Clustering, DBSCAN
Assessment Elements
- Midterm TestThese are individualized tests. The assessment of the test is based on the marking scheme that comes with the test assignment. Each problem and their sub parts are worth a certain number of points, the sum of these points is equal to 10, which is the maximum grade for the test on the 10 point scale. The student is awarded the assigned number of points for the correct answer to each part of the question and partial credit may also be awarded. The grade for the current category is calculated as cumulative from the beginning of the course. The test is conducted with the help of Safe Exam Browser.
- QuizzesThe grade for the current category is calculated as cumulative from the beginning of the course. Quizzes are conducted with the help of Safe Exam Browser. Two lowest grades for quizzes are not taken into account. There is no retake for quizzes. In each seminar group, the seminarian can single out individual students for seminar activity, adding a 0.01 bonus to the final grade.
- Kaggle CompetitionsThe home assignment is conducted in the form of a kaggle competition.
- Final TestThese are individualized tests. The assessment of the test is based on the marking scheme that comes with the test assignment. Each problem and their sub parts are worth a certain number of points, the sum of these points is equal to 10, which is the maximum grade for the test on the 10 point scale. The student is awarded the assigned number of points for the correct answer to each part of the question and partial credit may also be awarded. The grade for the current category is calculated as cumulative from the beginning of the course. The test is conducted with the help of Safe Exam Browser.
- Home AssignmentsHome assignments. The grade for the current category is calculated as cumulative from the beginning of the course.
- HackathonThe exam is organised in the form of Hackathon.
Interim Assessment
- 2026/2027 4th module0.15 * Hackathon + 0.2 * Quizzes + 0.25 * Kaggle Competitions + 0.15 * Midterm Test + 0.15 * Final Test + 0.1 * Home Assignments
Bibliography
Recommended Core Bibliography
- An Introduction to Statistical Learning, with Applications in Python, Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor, Springer Nature Switzerland AG 2023, 978-3-031-38747-0, published: 30 June 2023
Recommended Additional Bibliography
- The elements of statistical learning : data mining, inference, and prediction, Hastie, T., 2017