A tárgy címe:
Artificial Inteligence in Data Science
Elsődleges képzés:
Fizikus mérnök BSc
Kredit:
5
A tárgy besorolása:
compulsory
Óraszám - előadás:
1
Óraszám - gyakorlat:
2
Óraszám - labor:
0
Egyéb oktatás:
Számonkérés módja:
Coursework grade
Egyéb számonkérés:
Home and project works
Félév:
7
Előtanulmányi feltételek:
Introduction to Numerical Algorithms
Tantárgy felelőse:
Dr. János Török, associate professor, PhD
További oktatók:
Tárgyleírás:
Study and application to data the different types of machine learning methods
Subjects:
Basic image processing
Threshold methods, Otsu threshold
K-means clustering
Segmentation
Pattern matching
Feedforward neural networks
Backpropagation
Convolutional neural networks
Recurrent neural networks , LSTM networks
Transfer learning
Reinforcement learning
Applications:
Number recognition
Backpropagation implemented with matrix operations
Neural network frameworks (scikit-learn, keras, tensorflow, pytorch)
Temporal prediction
Text analysis, word gram
learning games, Q-learning
Ajánlott irodalom:
Michelucci, Umberto. Advanced applied deep learning: convolutional neural networks and object detection. Apress, 2019. ISBN-13: 978-1484249758
Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016, https://www.deeplearningbook.org/
https://keras.io/getting_started/intro_to_keras_for_researchers/
Kompetenciák:
Please find the detailed list, as quoted from the Hungarian training and outcome requirements of the Physicist Engineer program, in the Hungarian version of the course description.