The class focuses on understanding theories, applications, and impact of algorithms that fall in the broader field of artificial intelligence. Students learn by case studies, examples, and hands-on coding classes the capabilities, limits and issues of artificial intelligence. The course introduces basic theoretical frameworks and methods of artificial intelligence, as well as social, ethical and political consequences of the use of algorithms today.

The class focuses on how to tell stories with data to understand the political world and create social and political change. Students learn by case studies, examples, and hands-on work with tools and technologies. The class introduces basic methods for research, cleaning and analyzing datasets, but the focus is on ​deploying a full data science project for political purposes​. Overall, it consider the chances data science offers in understanding politics, presenting political processes and achieve political problem solving. Projects might be data-based research papers, artworks, journalistic investigations or any other form of data presentation..

The class provides an introduction to basic machine learning algorithms. From linear regression to random forests and from PCA to hierarchical clustering, students learn techniques for supervised and unsupervised learning. This done by interaction with the main theoretical concepts of each algorithm, as well as hands-on R programming sessions. Aim is that: 1. The students have a first understanding of machine learning, 2. Be able to implement, analyze and evaluate simple datasets based on the application of data-intensive algorithms.

The class focuses on the deployment of prominent deep learning algorithms for natural language processing. Students learn to use text corpora, their cleaning and processing by developing pipelines for efficient text manipulation and feature extraction. They apply basic architectures as RNNs and CNNs, and more advanced as transformers. The class provides examples a.o. on regression, classification, text generation and summarization. The students come in contact with the state-of-the-art of deep learning for natural language processing, recognizing limits and possibilities. During the course , students work in groups and develop deep neural models for text-related research questions.

The class provides students a detailed introduction to neural networks and deep learning. It introduces the detailed mathematical foundations of basic neural architectures, as feed forward networks, LSTMs, GRUs and CNNs. It investigates forward and backward propagation, as well as different optimization techniques. By the end of the course, students have full knowledge on the mathematical background of neural networks and are able to understand technical possibilities and limitations for developing more complex neural architectures.

The class analyzes political, economic, sociological and philosophical aspects of Big Data. It investigates issues regarding data privacy and property, social media platforms, and tech companies’ business models. It also studies the epistemological shifts associated with Big Data in science. The students learn about the impact, social and political consequences of the application of data-intensive algorithms in automated decision making, political campaigning, robotics and other fields.