This course covers advanced topics in deep learning architectures for natural language processing. The focus is on attention-based architectures, structure processing and variational-Bayesian approaches, and why these models are particularly suited to the ...
Machine learning methods are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented. ...
Natural language processing is ubiquitous in modern intelligent technologies, serving as a foundation for language translators, virtual assistants, search engines, and many more. In this course, students will learn algorithmic tools for tackling problems i ...
This course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the data science world (pandas, scikit-learn, Spark, etc.) ...
The Human Language Technology (HLT) course introduces methods and applications for language processing and generation, using statistical learning and neural networks. ...
This course introduces the core concepts and methodologies of Digital Humanities, integrating both theoretical and practical perspectives. Students learn to work with large-scale cultural datasets, acquiring the tools and techniques required for their proc ...
This course introduces the foundations of information retrieval, data mining and knowledge bases, which constitute the foundations of today's Web-based distributed information systems. ...
This course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and implement methods to analyze diverse data types, such as imag ...