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   +(36) 88 624 023 | |    H-8200, Veszprem, Egyetem str. 10, Building I.

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Ágnes Vathy-Fogarassy, PhD

- Artificial intelligence (MSc)

Topics covered

Students will learn about the following topics, including their individual training plans and interests:

T1. Neural Networks 

  • teaching neural networks, backpropagation, parameters, hyper-parameters  and hyperparameter-tuning, regularisation, optimizing convergence

T2. Recurrent Neural Networks (RNN)

  • the structure of RNNs, the effect of recurrent links, types of RNNs and their application, Bidirectional RNN (BRNN), Deep RNN (DRNN)
  • Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM) 

T3. Other types of networks and their application 

  • neural networks in ranking: RankNet, LambdaRank, LambdaMART
  • siamese neural networks
  • few-shot learning, one-shot learning


  1. Ian Goodfellow, Yoshua Bengio, Aaron Courville: Deep Learning, MIT Press (2016)
  2. Stanford University: Recurrent Neural Networks