Бакалавриат
2025/2026
Машинное обучение 1
Статус:
Курс обязательный (Прикладной анализ данных)
Где читается:
Факультет компьютерных наук
Когда читается:
3-й курс, 1, 2 модуль
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
3
Контактные часы:
56
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 and several topics in deep learning, such as artificial neural networks, recurrent neural networks, convolutional neural networks, transformers and attention mechanisms, auto-encoders, etc. The first two modules (Sep-Dec) DSBA and ICEF students apply Python programming language and popular packages, such as pandas, scikit-learn and TensorFlow, to investigate and visualize datasets and develop machine learning models that solve theoretical and data-driven problems. The next two modules (Jan-Jun) DSBA/ICEF students apply R programming language and dive deeper into mathematical, statistical, and algorithmic concepts. 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.
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
- 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.
- Home AssignmentsHome assignments. The grade for the current category is calculated as cumulative from the beginning of the course.
- Hackathon (Exam)The exam is organised in the form of Hackathon.
- 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.
- 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.
- Kaggle CompetitionsThe home assignment is conducted in the form of a kaggle competition.
Interim Assessment
- 2025/2026 2nd module0.15 * Final Test + 0.15 * Hackathon (Exam) + 0.1 * Home Assignments + 0.25 * Kaggle Competitions + 0.15 * Midterm Test + 0.2 * Quizzes
Bibliography
Recommended Core Bibliography
- Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, & Maintainer Trevor Hastie. (2013). Type Package Title Data for An Introduction to Statistical Learning with Applications in R Version 1.0. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.28D80286
Recommended Additional Bibliography
- Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The Elements of Statistical Learning : Data Mining, Inference, and Prediction (Vol. Second edition, corrected 7th printing). New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=277008