Παρουσίαση/Προβολή
Τεχνητή Νοημοσύνη ΙΙ
(ΥΣ19) - Εμμανουήλ Κουμπαράκης
Περιγραφή Μαθήματος
The course concentrates on the study of deep learning techniques and their use in natural language processing.
Topics: introduction to machine learning, regression, perceptron, neural networks, backpropagation, word vectors, word2vec and related models, dependency parsing, language modeling and RNNs, vanishing gradients and fancy RNNs, machine translation, seq2seq and attention, question answering, convolutional networks for NLP, contextual word embeddings, transformers, BERT, GPT-3 and related models, natural language generation, question answering for knowledge graphs, coreference resolution, dialogue systems and chatbots.
The programming exercises of the course are done using Python/PyTorch.
Piazza signup link: https://piazza.com/uoa.gr/fall2023/ys19 Important!
Ημερομηνία δημιουργίας
Δευτέρα 31 Αυγούστου 2020
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Διδάσκοντες
Διδάσκων:
Μανόλης Κουμπαράκης 
(koubarak [papaki] di.uoa.gr)Βοηθοί:
Σέργιος-Ανέστης Κεφαλίδης (s.kefalidis [papaki] di.uoa.gr)Κωνσταντίνος Νικολέτος (k.nikoletos [papaki] di.uoa.gr)Δέσποινα-Αθανασία Πανταζή (dpantazi [papaki] di.uoa.gr)Μαρία Τσούρμα (mtsourma [papaki] iti.gr)Μέθοδοι διδασκαλίας
Lectures: Friday 10:00 - 13:00 (Z)
Tutorials: Wednesday 17:00 - 18:00 (ΣΤ)Piazza signup link: https://piazza.com/uoa.gr/fall2023/ys19 Important!
Μέθοδοι αξιολόγησης
4 programming exercises using Python/PyTorch: 100%
Μαθησιακοί στόχοι
Upon successful completion of the course the students will be able to:
- Solve problems requiring text processing or natural language processing using neural networks.
- Use neural networks in other areas (e.g., Computer Vision).
- Develop machine learning systems using Python/TensorFlow/PyTorch.
Προαπαιτούμενα
Τεχνητή Νοημοσύνη Ι