Master
2024/2025
Machine Learning and AI in the World Economy
Type:
Elective course (World Economy)
Area of studies:
Economics
Delivered by:
School of World Economy
When:
2 year, 1, 2 module
Mode of studies:
offline
Open to:
students of all HSE University campuses
Instructors:
Seungmin Jin
Master’s programme:
World Economy
Language:
English
ECTS credits:
6
Course Syllabus
Abstract
Unlock the power of AI to shape the future of the global economy.This course aims to introduce students to the fundamentals of data manipulation and analysis, including an introduction to machine learning techniques, generative models, and basic neural network methods useful in the context of world economics. Students will engage in hands-on sessions using no-code/low-code AI tools such as KNIME* and explore how to use tools like ChatGPT to minimize coding and analyze data. The course will cover the technical aspects of AI, including practical implementation techniques, while addressing the ethical considerations, bias, and sustainability issues associated with AI.The course also includes seminars on the impact of large language models (LLMs) and other advanced deep learning technologies on the global economy, analyzing their effects on tasks such as coding and data analysis. Students will review recent research and developments in the field to gain a comprehensive understanding of how these AI advancements are shaping various economic sectors. By the end of the course, students will have acquired the knowledge and skills necessary to apply AI technologies in real-world economic scenarios, fostering innovation and strategic decision-making in the global economy.*About KNIME: KNIME is an open-source platform for data analytics, reporting, and integration. It provides a visual interface for building data workflows and implementing machine learning and AI solutions without the need for extensive coding. KNIME is particularly suitable for professionals in non-computer science domains, allowing them to harness the power of AI for data analysis and decision-making.
Learning Objectives
- The primary objective of this course is to equip students with a comprehensive understanding of the fundamentals and applications of machine learning and artificial intelligence (AI) in the global economy. By the end of this course, students will: 1. Understand Core AI Concepts: Gain a solid foundation in the principles and techniques of machine learning and AI, including data manipulation, neural networks, and generative models. 2. Practical Skills with AI Tools: Develop hands-on experience using no-code/low-code AI tools such as KNIME and ChatGPT to implement AI solutions, minimizing the need for extensive coding. 3. Impact of AI on the Economy: Analyze the economic impact of large language models (LLMs) and other advanced deep learning technologies, understanding their effects on tasks such as coding, data analysis, and strategic decision-making. 4. Ethical Considerations: Explore the ethical implications, biases, and sustainability issues associated with AI applications in the global economy. 5. Current Research and Trends: Review and discuss recent research and developments in AI to understand how these advancements are shaping various economic sectors. 6. Innovation and Strategy: Learn how AI technologies drive innovation and strategic planning in global markets, fostering an environment of continuous improvement and competitiveness. 7. Application in Real-world Scenarios: Apply AI technologies to real-world economic scenarios, enhancing students' ability to leverage AI for practical problem-solving and business innovation. By achieving these objectives, students will be well-prepared to harness the power of AI to influence and innovate within the global economic landscape.
Expected Learning Outcomes
- Grasp AI Fundamentals: Understand the foundational concepts of machine learning and artificial intelligence, including data manipulation, neural networks, generative models, and natural language processing.
- Utilize AI Tools: Demonstrate proficiency in using no-code/low-code AI tools like KNIME and ChatGPT to develop and implement AI solutions, reducing the reliance on extensive coding skills.
- Evaluate Economic Impact: Analyze the impact of advanced AI technologies, such as large language models (LLMs), on global economic activities, particularly in tasks like data analysis, and strategic decision-making.
- Address Ethical Concerns: Identify and discuss the ethical considerations, biases, and sustainability issues related to the deployment and use of AI in various economic sectors.
- Engage with Current Research: Critically review and engage with recent research and developments in the field of AI, understanding their implications for the global economy.
- Foster Innovation: Apply AI technologies to drive innovation and strategic planning in economic contexts, enhancing competitiveness and efficiency in global markets
- Apply in Real-world Contexts: Implement AI technologies in real-world economic scenarios, leveraging AI for practical problem-solving and business innovation.
- Communicate Effectively: Present and discuss AI-related topics and their economic implications clearly and effectively, both in written and oral formats.
Course Contents
- PART 1: Fundamentals of AI and Its Applications in the Global Economy
- Introduction to AI Fundamentals – Principles and Concepts
- Hands-on AI with KNIME
- AI in the Global Economy: Case Studies (6 hours)
- Ethical Considerations and AI (2 hours)
- PART 2: Advanced Applications and Research Seminar (24 hours)
- Hands-on AI with Generative AI / Prompt Engineering
- Research Seminar
- Global AI Revolution and Its Impact on the Economy
- AI and Strategy: Impact and Cases
- AI Impact on Law and Policy
- AI and Sustainability: Impact and Cases
- Group Project Work and Presentations
Assessment Elements
- Class participation and KNIME exercises
- Case study discussions and team project using KNIME
- Class participation, engagement and review presentation
- Project work
- Final Exam
Interim Assessment
- 2024/2025 2nd module0.2 * Case study discussions and team project using KNIME + 0.1 * Class participation and KNIME exercises + 0.1 * Class participation, engagement and review presentation + 0.4 * Final Exam + 0.2 * Project work
Bibliography
Recommended Core Bibliography
- Машинное обучение с использованием библиотеки H2O : мощные и масштабируемые методы для глубокого обучения и ИИ, Кук, Д., 2018
- Прагматичный ИИ : машинное обучение и облачные технологии, Гифт, Н., 2019
Recommended Additional Bibliography
- Машинное обучение с участием человека : активное обучение и аннотирование для ориентированного на человека искусственного интеллекта, Монарх (Манро), Р., 2022