2025/2026
Машинное обучение 2
Статус:
Маго-лего
Где читается:
Факультет компьютерных наук
Когда читается:
1, 2 модуль
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
6
Контактные часы:
48
Course Syllabus
Abstract
The Machine Learning 2 course provides advanced knowledge and practical skills in the field of artificial intelligence and data analysis. It covers methods and algorithms not normally considered in an introductory course and prepares students for more specialized subjects.
Learning Objectives
- To introduce students to the key concepts and techniques of machine learning and deep learning.
- To develop the skills of choosing and applying the most suitable algorithms for solving specific tasks.
- Teach you how to analyze, process, and interpret large amounts of data.
- Develop skills to evaluate and improve the quality of machine learning models
Expected Learning Outcomes
- Selects and applies suitable machine learning algorithms for different types of tasks
- Performs data preprocessing and analysis, including cleaning, normalization, and processing of missing values.
- Develops, trains, and evaluates classification and regression models.
- Applies clustering methods in practice to group data.
- Evaluates the quality of models using various metrics and cross-validation methods.
- Owns tools and frameworks for machine learning, such as scikit-learn and others.
- It takes into account the ethical aspects of using machine learning in model development.
- Integrates machine learning models into real business processes and solves practical problems in various fields.
Course Contents
- Introduction to neural networks
- Optimization of neural networks
- Time series analysis
- Recommendation systems
- Outflow forecasting
- Estimation of uncertainty
Bibliography
Recommended Core Bibliography
- Jason Bell. (2020). Machine Learning : Hands-On for Developers and Technical Professionals: Vol. Second edition. Wiley.
- Linden, A., & Yarnold, P. R. (2018). Using machine learning to evaluate treatment effects in multiple-group interrupted time series analysis. Journal Of Evaluation In Clinical Practice, 24(4), 740–744. https://doi.org/10.1111/jep.12966
Recommended Additional Bibliography
- Fletcher, T. S. B. (2012). Machine learning for financial market prediction. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsndl&AN=edsndl.oai.union.ndltd.org.bl.uk.oai.ethos.bl.uk.565794