| Osaamistavoitteet |
Students understand the theory of neural networks, Nature-Inspired Computing (NIC) methods, and deep learning algorithms. Students get familiar with the most popular neural networks models (Perceptron, Adaline, multilayer neural networks, Self-Organizing Map (SOM), etc.) and their learning algorithms (Widrow-Hoff rule, Back-Propagation (BP) training, Back-propagation Through Time (BTT) training, competitive learning, etc.). Students master the essential knowledge of deep learning techniques, such as regularization, momentum, batch normalization, and dropout. Students understand the principles, structures, and algorithms of typical deep learning neural networks, e.g., Convolutional Neural Networks (CNN). Students know how to build up deep learning algorithms from scratch. Students gain the hand-on experiences in using deep learning techniques to deal with practical problems |
| Sisältö |
Elementary concepts and challenges of machine learning. Neural networks models (linear neural networks, feedforward neural networks, recurrent neural networks, Self-Organizing Map (SOM), etc.). Deep reinforcement learning. Concepts and challenges of deep learning. Deep learning models and techniques (deep neural networks, Convolutional Neural Networks (CNN), AutoEncoder, Long Short Term Memory (LSTM), etc.). Applications of deep learning in classification, prediction, pattern recognition, etc. |
| Suoritustavat |
Lectures, teaching materials, exercises, and examination |
| Toteutustavat |
distance teaching and study |
| Oppimateriaalit |
II. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2017, A. P. Engelbrecht, Fundamentals of Computational Swarm Intelligence, John Wiley & Sons Ltd, 2005, S. Haykin, Neural Networks, A Comprehensive Foundation, Prentice Hall, 2008, lecture slides and notes, selected papers from journals and conference proceedings |
| Arvosteluperusteet |
Grading (80% examination and 20% computer exercises): 0-5 |
| Edellytykset |
Basic knowledge of linear algebra and probability theory |
| Ajankohta |
Fall semester |
| Tarjontatieto |
This course is open to everyone |
| Lisätietoja |
Teaching language: English |
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