1. Introduction to Artificial Intelligence (AI)
- History and development of AI
- Definitions and areas of AI (machine learning, deep learning, robotics, natural language processing, etc.)
- Overview of real-world applications of AI
2. Foundations of machine learning
- Overview of types of machine learning: supervised, unsupervised, reinforcement
- Basic concepts and terminology
- Simple machine learning algorithms and their applications
3. Brief background on neural networks, deep learning and applications
- Basic terminology of neural networks.
- Brief overview of architectures, perceptron, multilayer perceptrons, also deep neural networks and their architectures (CNN, RNN, LSTM)
- Applications of deep learning
4. Natural Language Processing (NLP)
- Introduction to NLP and its applications (chatbots, automatic machine translation)
- NLP techniques and models (tokenization, word embeddings, transformer models)
- Introduction to BERT and GPT models (transformer architectures)
5. Generative models in AI
- Introduction to generative models and their principles
- Generative adversarial networks (GANs) and their applications
6. Generative models in AI II
- Generative AI in software engineering and technical fields
- Examples of the use of generative AI in graphics, design and other fields
7. AI and game theory, cognitive systems and artificial life, robotics
- Game theory and its relation to AI
- Application of game theory in AI for conflict resolution, optimization and decision making (business strategy, social simulation)
- Foundations of cognitive systems and their inspiration by human thinking, relation to AI
- Introduction to artificial life (Alife) and its goals: simulation of life using AI, examples of Alife projects.
- Robotics and its integration with AI, swarm intelligence
8. Decision systems and optimization
- Introduction to decision systems and optimization algorithms in AI
- Examples of applications in logistics and planning
- AI in dynamic environments
9. Data visualization and interpretability
- What is Explainable Artificial Intelligence (XAI)
- The importance of interpretability of AI models (white box/glass box/black box)
- Examples of tools and methods to improve transparency and visualization to better understand data and model results
10 The future of AI, ethics and societal implications of AI
- Trends and challenges in AI
- Possible developments and directions for AI in the coming years
- Ethical dilemmas (autonomous systems, surveillance)
- Bias and fairness in algorithms
- History and development of AI
- Definitions and areas of AI (machine learning, deep learning, robotics, natural language processing, etc.)
- Overview of real-world applications of AI
2. Foundations of machine learning
- Overview of types of machine learning: supervised, unsupervised, reinforcement
- Basic concepts and terminology
- Simple machine learning algorithms and their applications
3. Brief background on neural networks, deep learning and applications
- Basic terminology of neural networks.
- Brief overview of architectures, perceptron, multilayer perceptrons, also deep neural networks and their architectures (CNN, RNN, LSTM)
- Applications of deep learning
4. Natural Language Processing (NLP)
- Introduction to NLP and its applications (chatbots, automatic machine translation)
- NLP techniques and models (tokenization, word embeddings, transformer models)
- Introduction to BERT and GPT models (transformer architectures)
5. Generative models in AI
- Introduction to generative models and their principles
- Generative adversarial networks (GANs) and their applications
6. Generative models in AI II
- Generative AI in software engineering and technical fields
- Examples of the use of generative AI in graphics, design and other fields
7. AI and game theory, cognitive systems and artificial life, robotics
- Game theory and its relation to AI
- Application of game theory in AI for conflict resolution, optimization and decision making (business strategy, social simulation)
- Foundations of cognitive systems and their inspiration by human thinking, relation to AI
- Introduction to artificial life (Alife) and its goals: simulation of life using AI, examples of Alife projects.
- Robotics and its integration with AI, swarm intelligence
8. Decision systems and optimization
- Introduction to decision systems and optimization algorithms in AI
- Examples of applications in logistics and planning
- AI in dynamic environments
9. Data visualization and interpretability
- What is Explainable Artificial Intelligence (XAI)
- The importance of interpretability of AI models (white box/glass box/black box)
- Examples of tools and methods to improve transparency and visualization to better understand data and model results
10 The future of AI, ethics and societal implications of AI
- Trends and challenges in AI
- Possible developments and directions for AI in the coming years
- Ethical dilemmas (autonomous systems, surveillance)
- Bias and fairness in algorithms