Магистратура
2026/2027
Предсказательное моделирование
Статус:
Курс по выбору (Бизнес-аналитика и системы больших данных)
Кто читает:
Департамент математики
Где читается:
Факультет экономических наук
Когда читается:
2-й курс, 1, 2 модуль
Охват аудитории:
для всех кампусов НИУ ВШЭ
Преподаватели:
Швыдун Сергей Владимирович
Язык:
английский
Кредиты:
6
Контактные часы:
48
Course Syllabus
Abstract
Predictive Modeling is a statistical subject taught to the second year graduate students over the first and second academic modules. The material ranges from classical topics such as linear and non-linear regression and classification to less frequently discussed questions such as Markov Chain Monte-Carlo, dynamic linear models, multivariate time series analysis, etc. For each model considered, much attention is paid to performance assessment so as to minimize the forecast error. Throughout the course a certain balance between mathematical rigor and intuition has to be maintained. Often, this dilemma is resolved in favor of illustrative examples which help students capture the main idea and learn how to use it in practice instead of memorizing derivations. Nonetheless, we find it instructive to provide brief and tractable proofs whenever it makes pedagogical or some other sense. Some not too hard theoretical questions are left for home assignments which makes students work with pen and paper and provides a deeper understanding of underlying theory. The practice skills are developed throughout in-class practice sessions and home assignments involving real-life datasets.
Learning Objectives
- Predictive Modeling gives insight into machine learning algorithms with emphasis on assessing accuracy of prediction and selecting among the models. Another indirect purpose of the course is to guide the students' research by suggesting more challenging topics and problems to the interested students. This kind of activity develops self-study skills and critical thinking, highlights the importance of literature review and many more.
Expected Learning Outcomes
- Acquire the skills to use R/Python functions from different R/Python packages to pre-process the input
- Apply the knowledge and tools of predictive analytics to real-life applications
- Be aware of practical applications of predictive modeling from science to business
- Be aware of understand theory behind predictive modeling, types of predictive models, key steps of model creation and evaluation
- Know how to implement different types of models in the R/Python programming language
Course Contents
- Introduction
- Predictive modeling process
- Reducing the dimension
- Regression models
- Time series analysis
- Classification models
- Clustering
- Markov Chain Monte Carlo methods
- Dynamic linear models
Assessment Elements
- Home assignmentThe home assignment is completed individually. Each student is required to complete the assigned task, prepare a report containing the solution and submit the report and all required files to the instructor before the specified deadline.
- Class activity 1The class activity is completed individually during the class. Students are required to complete the assigned exercises using their computers and submit a report together with the corresponding code and any other required files by the end of the class.
- ExamThe exam is completed individually and consists of a set of tasks and/or exercises related to the topics covered in the course. The exam is open-book, and students may use materials, software, and other resources permitted by the instructor. During the exam, students must follow the examination and proctoring rules specified by the instructor and are not allowed to communicate with other persons or use unauthorized resources or devices. Any violation of the examination or proctoring rules may be handled in accordance with the applicable university regulations and academic integrity policies.
- Class activity 2The class activity is completed individually during the class. Students are required to complete the assigned exercises using their computers and submit a report together with the corresponding code and any other required files by the end of the class.
- Class activity 3The class activity is completed individually during the class. Students are required to complete the assigned exercises using their computers and submit a report together with the corresponding code and any other required files by the end of the class.
Interim Assessment
- 2026/2027 2nd module0.3 * Exam + 0.15 * Class activity 3 + 0.15 * Class activity 2 + 0.15 * Class activity 1 + 0.25 * Home assignment
Bibliography
Recommended Core Bibliography
- 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
- V Kishore Ayyadevara. (2018). Pro Machine Learning Algorithms : A Hands-On Approach to Implementing Algorithms in Python and R. Apress.
Recommended Additional Bibliography
- Deepti Gupta. (2018). Applied Analytics Through Case Studies Using SAS and R : Implementing Predictive Models and Machine Learning Techniques. Apress.
- Miroslav Kubat. (2017). An Introduction to Machine Learning (Vol. 2nd ed. 2017). Springer.