2025/2026
Машинное обучение
Лучший по критерию «Полезность курса для Вашей будущей карьеры»
Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»
Лучший по критерию «Новизна полученных знаний»
Статус:
Маго-лего
Когда читается:
3 модуль
Онлайн-часы:
40
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
3
Контактные часы:
8
Course Syllabus
Abstract
This course introduces the fundamental methods of modern machine learning with an emphasis on practical implementation. Students learn the complete machine learning workflow, including data preparation, model development, evaluation, optimization, explainability, reproducibility, and production deployment. The course combines theoretical foundations with hands-on programming in Python, preparing students to develop, deploy, and maintain machine learning systems using contemporary tools and best practices.
Learning Objectives
- The course gives students an important foundation to develop and conduct their own research as well as to evaluate research of others.
Expected Learning Outcomes
- Be able to apply the basic concepts from machine learning theory
- Be able to identify appropriately the type of a machine learning problem at hand, e.g. classification, regression, clustering
- Be able to differentiate between supervised and unsupervised learning methods, understand their benefits and limitations
- Be able to master theoretical understanding of key methods for supervised learning to apply decision trees, linear regression, logistic regression, quantile regression, variations of regression for non-Gaussian distributions of the target variable
- Be able to differentiate and correctly apply most common approaches to ensemble learning (random forests, gradient boosting, stacking, blending, etc.) as well as to explain their benefits and limitations
- Be able to identify and tackle issues related to overfitting and model instability
- Be able to apply basic tools and approaches to automated text processing as well as to incorporate text data into machine learning solutions
- Be able to systematize and prioritize best practices in experiment tracking and sustainable ML development
Interim Assessment
- 2025/2026 3rd module0.3 * Graded quizzes + 0.5 * Final project + 0.2 * Mid-term homework
Bibliography
Recommended Core Bibliography
- Harman, G., & Kulkarni, S. (2007). Reliable Reasoning : Induction and Statistical Learning Theory. Cambridge, Mass: A Bradford Book. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=189264
- Kulkarni, S., Harman, G., & Wiley InterScience (Online service). (2011). An Elementary Introduction to Statistical Learning Theory. Hoboken, N.J.: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=391376
- Mehryar Mohri, Afshin Rostamizadeh, & Ameet Talwalkar. (2018). Foundations of Machine Learning, Second Edition. The MIT Press.
- Miroslav Kubat. (2017). An Introduction to Machine Learning (Vol. 2nd ed. 2017). Springer.
Recommended Additional Bibliography
- Haroon, D. (2017). Python Machine Learning Case Studies : Five Case Studies for the Data Scientist. [Berkeley, CA]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1623520
- Lantz, B. (2019). Machine Learning with R : Expert Techniques for Predictive Modeling, 3rd Edition (Vol. Third edition). Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2106304
- Murphy, K. P. (2012). Machine Learning : A Probabilistic Perspective. Cambridge, Mass: The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=480968
- Ramasubramanian, K., & Singh, A. (2017). Machine Learning Using R. [Place of publication not identified]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1402990
- Sarkar, D., Bali, R., & Sharma, T. (2018). Practical Machine Learning with Python : A Problem-Solver’s Guide to Building Real-World Intelligent Systems. [United States]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1667293