Modern Methods of Decision Making

2023/2024

Учебный год

ENG

Обучение ведется на английском языке

6

Кредиты

Кто читает:

Департамент анализа данных и искусственного интеллекта

Статус:

Курс обязательный

Когда читается:

1-й курс, 3, 4 модуль

Преподаватели

Громов Василий Александрович

Томащук Корней Кириллович

Course Syllabus

Abstract

This course is a required foundational course for masters’ students in “Data Science” program, designed to familiarize them with the most recent methods of decision making. The course is divided into two parts. The first part tells about how a person makes decisions and covers the following topics: mathematical modelling, genetic algorithms, multi-criteria optimization. The second part of the course covers topic which explain how AI makes decisions or in more details how human makes decisions with a help of AI. The set of topics considered in this part of the course: multi-agent systems, prompt engineering, theoretical and informational foundations of machine learning.

Learning Objectives

• The course gives students an important foundation to tackle real-life problems using classical applied math approaches as well as with a help of AI.

Expected Learning Outcomes

• Learn how formulate a basic mathematical model for a problem presented in text form.
• Learn some basic genetic algorithms and how to use the for solving optimization problems.
• Learn main principals of prompt engineering
• Learn how to formulate and solve basic multi-criteria optimization problem.

Course Contents

• Mathematical modeling
• Genetic algorithms
• Multi-criteria optimization
• Multi-agent systems
• Prompt engineering
• Theoretical and informational foundations of machine learning

Assessment Elements

• Лабораторная 1
• Лабораторная 2
• Коллоквиум 1
• Коллоквиум 2

Interim Assessment

• 2023/2024 4th module
  Final = 0.25Л1 + 0.25Л2 + 0.25К1 + 0.25K2

Bibliography

Recommended Core Bibliography

• Многокритериальные модели формирования и выбора вариантов систем, Дубов, Ю. А., 1986

Recommended Additional Bibliography

• Современные алгоритмы поисковой оптимизации : алгоритмы , вдохновленные природой : учебное пособие, Карпенко, А. П., 2014