μαθηματα Μεταπτυχιακου 

Introduction to Artificial Intelligence

• Overview of AI, history, and fundamental concepts.

Machine Learning Basics

• Introduction to machine learning algorithms and their applications.

Data Science and Analytics

• Fundamentals of data handling, processing, and analysis.

Neural Networks and Deep Learning

• Exploring the architecture and functioning of neural networks.

AI Programming

• Programming for AI using languages like Python, including libraries and frameworks.

Ethics in AI

• Discussing ethical considerations in AI, such as bias and fairness.

AI and Society

• Exploring the societal impacts of AI, including socio-technical systems.

Legal Frameworks in AI

• Overview of laws and regulations affecting AI development and use.

Data Privacy and Security

• Understanding data protection, privacy laws, and cybersecurity in AI.

Intellectual Property Rights in AI

• Navigating IP issues in AI innovation and development.

Human-Centric AI Design

• Principles of designing AI with a focus on human needs and values.

Sustainable AI Development

• Studying AI's role in sustainable development and environmental impact.

AI Governance and Policy

• Governance structures and policy-making for AI.

Bias and Fairness in AI Systems

• Identifying and mitigating biases in AI algorithms.

AI in Public Sector

• Understanding AI applications in government and public services.

AI in Healthcare: Legal and Ethical Aspects

• Special focus on AI in healthcare, including legal and ethical challenges.

AI and Future of Work

• Exploring how AI is transforming the job market and workplace dynamics.

Practical AI Project Management

• Managing AI projects, including planning, execution, and ethics.

Case Studies in Lawful Chreotechnic AI

• Examining real-world examples and case studies.

Capstone Project in Lawful Chreotechnic AI

• A comprehensive project that integrates the knowledge and skills acquired.