Бакалавриат 2025/2026

Машинное обучение

Статус: Курс по выбору (Международная программа по экономике и финансам)

Кто читает: Международный институт экономики и финансов

Когда читается: 4-й курс, 1-4 модуль

Охват аудитории: для своего кампуса

Преподаватели: Сохраби Маджид

Язык: английский

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 dive deeper into mathematical, statistical, and algorithmic concepts and studying deep neural networks. During the entire period of study, students participate in Kaggle competitions in groups, a widespread format in data science, aiming at developing soft skills such as collaborative work, solving a research task, meeting the deadlines. 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.