Thesis supervisor: László Kovács
Location of studies (in Hungarian): Debreceni Egyetem Informatikai Kar Abbreviation of location of studies: ITDI
Description of the research topic:
The research topic focuses on multivariate data analysis using advanced machine learning methods. In particular, the coherent interpretation of signals from different types of sensors and detectors using state-of-the-art machine learning methods. A characteristic of the sensor data obtained in this way is that processing a large amount of data available is far from being a trivial task, and noise filtering is therefore given a prominent role as a pre-processing step. In addition, the use of advanced machine learning algorithms such as deep learning and acceleration of the technology on state-of-the-art dedicated embedded processors (GPU, FPGA) is inevitable. The aim is to use the incoming data and machine learning methods to achieve accurate decision support, pattern recognition and automation in the target domains applied. Such target areas could be medical computing, self-driving vehicles, smart city environments, (particle) physics, and space physics observations. As part of the research, we will focus on state-of-the-art deep learning model building, optimization of learning hyperparameters, and fusion in systems, including possible physical implementation.
Irodalom:
1. Yoshua Bengio, Ian J. Goodfellow, Aaron Courville: Deep Learning, MIT Press, 2015.
2. I. Goodfellow, Y. Bengio, A. Courville: Deep Learning, MIT Press, 2016.
3. F. Chollet: Deep learning with Python, Manning, November 2017.
4. Terrence J. Sejnowski: The Deep Learning Revolution, The MIT Press, 2018.
5. L. Igual, S. Segui: Introduction to Data Science, A Python Approach to Concepts, Techniques and Applications, Springer International Publishing, 2017.
6 C.C. Aggarwal: Neural Networks and Deep Learning, Springer International Publishing, 2018.
Deadline for application: 2022-11-15
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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