| Artificial Intelligence (AI) | The general name for systems that mimic human intelligence. They can perform tasks such as problem-solving, learning, and decision-making. |
| Machine Learning (ML) | It is a sub-branch of artificial intelligence that enables computers to learn from data and improve their performance over time. |
| Deep Learning (DL) | These are algorithms that mimic the neural networks in the human brain and can recognize patterns by analyzing large amounts of data. |
| Natural Language Processing (NLP) | It is the branch of artificial intelligence that deals with computers understanding, processing and producing human language. |
| Artificial Neural Networks (ANN) | They are mathematical models that mimic the working principle of nerve cells in the human brain and are used in learning processes. |
| Recursive Neural Networks (RNN) | It is a type of neural network that can remember past information by processing sequential data, especially used in language model and time series analysis. |
| Convolutional Neural Networks (CNN) | It is a neural network model optimized for image and video analysis. It is particularly used in object recognition and computer vision. |
| Large Language Models (LLM) | These are artificial intelligence models trained on very large datasets that can produce human-like text. Models like GPT and BERT, for example, fall into this category. |
| Generative AI (Generative Artificial Intelligence) | It refers to artificial intelligence models that can produce new content such as text, images, audio, and even code. |
| Data Mining | It is a set of techniques for extracting meaningful patterns and information from large data sets. |
| Feature Engineering | It is the process of extracting meaningful features from raw data to increase the accuracy of machine learning models. |
| Sentiment Analysis | It is an NLP technique used to determine the emotional tone (positive, negative, neutral) in text data. |
| Reinforcement Learning (RL) | It is a learning method that enables artificial intelligence to make decisions based on the reward-punishment mechanism. |
| Supervised Learning | It is a machine learning model that is trained with labeled data and learns the correct outputs corresponding to the inputs. |
| Unsupervised Learning | A type of machine learning that attempts to find patterns in unlabeled data. It includes techniques such as clustering and dimensionality reduction. |
| Artificial General Intelligence (AGI) | It is a theoretical type of artificial intelligence that has human-level cognitive abilities and can learn and perform different tasks. |
| Artificial Super Intelligence (ASI) | It is a hypothetical level of artificial intelligence that surpasses human intelligence, can think independently, and produce innovative solutions. |
| Bias and Fairness | It is the general name for studies aimed at preventing artificial intelligence from making unfair or discriminatory decisions due to biases in training data. |
| Fuzzy Logic | It is an artificial intelligence technique that develops human-like decision mechanisms that can process uncertainties rather than absolute truths or falsehoods. |
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