Бакалавриат
2025/2026
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 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.