2024/2025
Машинное обучение
ID 844524
Лучший по критерию «Полезность курса для Вашей будущей карьеры»
Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»
Статус:
Маго-лего
Когда читается:
3 модуль
Онлайн-часы:
40
Охват аудитории:
для своего кампуса
Преподаватели:
Мальцева Дарья Васильевна
Язык:
английский
Кредиты:
3
Course Syllabus
Abstract
This course provides a comprehensive introduction to the development and application of modern machine learning methods, covering the complete lifecycle of a machine learning solution from problem formulation and data preparation to model deployment and monitoring. The course addresses supervised and unsupervised learning, including classification, regression, clustering, anomaly detection, ensemble methods, and dimensionality reduction. Particular attention is given to data preprocessing and feature engineering, model evaluation and validation, hyperparameter optimization, and the prevention of overfitting and data leakage. The course also introduces contemporary practices for developing reliable and interpretable machine learning systems, including model explainability, bias and fairness assessment, experiment tracking, reproducibility, testing, and version and configuration management. The theoretical and methodological foundations are complemented by practical implementation in Python using widely adopted machine learning libraries and tools, enabling students to design, evaluate, interpret, and maintain end-to-end machine learning solutions.
Learning Objectives
- To provide students with a theoretical understanding of the fundamental principles of machine learning, including supervised and unsupervised learning paradigms, model generalization, overfitting, validation, and the principles underlying major machine learning algorithms.
- To develop a systematic methodological approach to solving machine learning problems, including problem formulation, data preprocessing and feature engineering, algorithm and metric selection, model evaluation, hyperparameter optimization, interpretation, and assessment of model reliability.
- To develop practical skills in designing and implementing end-to-end machine learning solutions, using Python and contemporary machine learning tools to build, compare, optimize, and interpret models for classification, regression, clustering, and other data-driven tasks.
- To provide students with the knowledge and practical foundations required to develop reproducible and production-oriented machine learning systems, including experiment tracking, testing, version and configuration management, model deployment principles, and monitoring of data quality, drift, and model performance.
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
- 2024/2025 3rd module0.5 * Final project + 0.3 * Graded quizzes + 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