Introduction to Artificial Intelligence

2025/2026

Учебный год

ENG

Обучение ведется на английском языке

3

Кредиты

Кто читает:

Департамент политологии и международных отношений

Статус:

Курс по выбору

Когда читается:

1-й курс, 4 модуль

Преподаватель

Глотов Алексей Александрович

Course Syllabus

Abstract

This course provides a comprehensive, hands-on introduction to the core paradigms of modern Artificial Intelligence. Designed for non-specialists, it bridges the gap between theoretical understanding and practical application. Students will first explore fundamental concepts of AI and machine learning, learning to build classic predictive models using AutoML tools. The course then shifts focus to the rapidly evolving field of Generative AI, where students will gain practical skills in prompt engineering, AI-augmented research, and visual information analysis. The emphasis is on developing the ability to effectively use AI as a tool to accelerate and enhance professional and research tasks across various disciplines.

Learning Objectives

• Aim is to bridge the gap between theoretical understanding and practical application: students will first explore fundamental concepts of AI and machine learning, learning to build classic predictive models using AutoML tools.

Expected Learning Outcomes

• Explains key concepts, historical context, and major classes of AI algorithms (supervised, unsupervised, generative)
• Formulates machine learning problems (classification, regression) and solve them using classical ML frameworks and AutoML platforms
• Applies Generative AI tools (LLMs, image generators) to accelerate research, analyze information, and create content
• Utilizes structured prompt engineering techniques to obtain reliable and high-quality outputs from AI models
• Critically assesses the capabilities, limitations, and ethical implications of applied AI solutions

Course Contents

• Demystifying AI: Foundations & Landscape
• Linear Models
• Decision Trees & Ensembles
• Automated Machine Learning
• Neural Networks on tabular data
• LLM basics and generative analytic tools
• LLM prompting
• LLM advanced
• AI that Sees: Computer Vision & Image Generation
• Visual Language Models

Assessment Elements

• Homework Assignments
  Mandatory. Focus on applying specific tools and concepts. Three practical tasks after Sessions 1, 3, 5, 7, 9, 12.
• Final Integrative Project
  A small project demonstrating the application of AI to a topic of the student's choice. andator y. Submitted in the form of a report/presentation.
• Activity & In-Class Tasks
  Participation in discussions, completion of in-workshop mini-tasks, peer reviews.

Interim Assessment

• 2025/2026 4th module
  0.1 * Activity & In-Class Tasks + 0.3 * Final Integrative Project + 0.6 * Homework Assignments

Bibliography

Recommended Core Bibliography

• Haenlein, M., & Kaplan, A. (2019). A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925

Recommended Additional Bibliography

• Osondu, O. (2021). A First Course in Artificial Intelligence. Bentham Science Publishers Ltd.