Róbert Lakatos

Róbert Lakatos

Computer and Data Scientist

Teaching

Introduction to Natural Language Text Processing

B.Sc course, University of Debrecen, Department of Data Science and Visualization, 2024

Within the framework of the subject, students will learn about the basics of natural language text processing (NLP). In addition, they also gain practical experience while solving various tasks. Main topics: logistic regression, naive Bayes model, PCA, n-gram models, Word2Vec, classical and recurrent neural networks. Furthermore, during the completion of the subject, students can gain insight into current, modern neural architectures. During the semester, students will also have the opportunity to test and train these architectures on real data using cloud-based services (Google Collab).

Artificial Intelligence in planning and decisionmaking

B.Sc course, University of Debrecen, Department of Data Science and Visualization, 2024

The subject aims for students to learn the principles of artificial intelligence (AI) and their application in business planning and decision-making. During the classes, students gain insight into various AI technologies, such as machine learning, predictive modeling, and optimization algorithms. Special emphasis is placed on real industry examples and case studies, with the help of which students understand how these tools can be applied to solve various business challenges.

Advanced Natural Language Processing

M.Sc course, University of Debrecen, Department of Data Science and Visualization, 2024

This course delves into advanced concepts of Natural Language Processing (NLP) and Machine Learning (ML) with a strong focus on modern deep learning techniques. It covers foundational topics such as tokenization, text representation, and pipelines, as well as cutting-edge research in large language models (LLMs), transformers, and their applications. The course emphasizes both theoretical understanding and practical implementation, preparing students to tackle real-world NLP challenges, including security, privacy, and human-centered design. During the semester, students will also have the opportunity to test and train these architectures on real data using cloud-based services (Google Collab).

Generative Networks

B.Sc course, University of Debrecen, Department of Data Science and Visualization, 2024

The course aims for students to gain deep knowledge of modern theoretical methods and technological implementations linked to generative methodologies. With the help of the necessary software and hardware device systems, students learn about the theoretical and practical backgrounds of the components of generative methodologies, basic generative networks in the PyTorch environment, DCGAN that also uses advanced convolution layers, GAN control, and the creation of conditional GANs. With the help of the course, students learn about advanced GAN programming in a practice-oriented way, such as data augmentation, protection of personal data, or the use of GAN applications in questionnaires. The course pays special attention to complex solutions, including measuring the comparability of generative models, realism and diversification, the detection of bias, or implementing different style transfer techniques (Pix2Pix, CycleGAN, StyleGAN). Students work on pre-agreed project tasks within the areas of generative methodology applications.

The Basics of Artificial Intelligence

B.Sc course, Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen, 2024

Artificial Intelligence (AI) has a rich history deeply rooted in scientific exploration and discovery. Beginning with its conceptualization in the mid-20th century, AI has evolved alongside advancements in computing power and cognitive science, shaping a diverse landscape of theories and applications.