| 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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ABOUT US
Mission
Our university's Artificial Intelligence mission aims to conduct pioneering work in the field of artificial intelligence by encouraging academic research, innovative applications, and interdisciplinary collaborations. By supporting the production of scientific knowledge, we aim to create an accessible and sustainable AI ecosystem for students, academics, and industry stakeholders.
Vision
Our university's Artificial Intelligence vision aims to support students and faculty in adapting to the demands of the digital age by encouraging the effective use of AI in education. By enhancing the quality of education through AI-powered learning environments, intelligent assistants, and data-driven educational solutions, we aim to be a center that strengthens our students' analytical thinking, problem-solving, and innovative approach development skills.
Support
You can send us all your questions and problems regarding Artificial Intelligence Services via the Solution Center e-mail address.
solutioncenter@ozyegin.edu.tr
Career Opportunities
Dear Student, Would you like to step into the world of Artificial Intelligence, gain experience in real projects, and improve yourself? 🎯 Join our Artificial Intelligence team intern, part-time or part-time By working as a , you can get a strong start to your career and take part in innovative projects that shape the future of our university! If you'd like to be a part of this exciting team, you can contact us at the address below.
ai-team@ozyegin.edu.tr
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info@ozyegin.edu.tr
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You can send us all your questions and problems regarding Artificial Intelligence Services via the Solution Center e-mail address.
solutioncenter@ozyegin.edu.tr
