Predictive Modelling

2024/2025

Academic Year

ENG

Instruction in English

6

ECTS credits

Delivered at:

Department of Mathematics

Course type:

Elective course

When:

2 year, 1, 2 module

Instructor

Shvydun, Sergey

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
• Clustering
• Classification models
• Markov Chain Monte Carlo methods
• Dynamic linear models

Assessment Elements

• Home assignments
  Should be done by students individually. Each student has to prepare a report and submit to instructors before the deadline. Some home assignments can be done in groups.
• Class activity
• Exam
  The exam consists of several questions. In some of them students should provide a short answer, in others they have to do a matching or answer the multiple-choice questions. The exam will be open-book, so students may use slides, Python, Google, home assignments, etc. on computer only. Students are not allowed to use a mobile phone or any other devices and communicate with classmates and any other people during the exam.

Interim Assessment

• 2024/2025 2nd module
  0.3 * Class activity + 0.3 * Exam + 0.4 * Home assignments

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.

Authors

• SHVYDUN SERGEY VLADIMIROVICH
• EGOROVA Liudmila GENNADEVNA
• Beklarian Armen Levonovich