Artificial Intelligence II

Semester:
7ο
Course Type:
Elective Specialization courses (ΠΜ-E)
Track:
CS (Computer Science)
Code:
ΥΣ19
ECTS:
6
TEACHING HOURS per week
Theory:
3
Seminar:
1
Laboratory:
-
Specializations
Foundations of Computer Science (Ε1):
-
Data and Knowledge Management (Ε2):
B Βασικό
Software (Ε3):
B Βασικό
Hardware and Architecture (Ε4):
-
Communications and Networking (Ε5):
-
Signal and Information Processing (Ε6):
-
Related Courses
Course Content

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 and related models, natural language generation, question answering for knowledge graphs.

The programming exercises of the course are done using Python/TensorFlow/PyTorch.