Computer Science

This course examines the risks to which information systems are exposed and explores preventive measures and corrective actions to address them. Based on the principle that security is everyone’s responsibility, the course takes a managerial rather than a technical approach, as risks to information system security are not limited to the technological risks of IT platforms or networks. They encompass risks related to the company’s organizational structure, work methods, legal and regulatory constraints, personnel, partners, and so on. The course provides the necessary knowledge of methods and tools to actively contribute to various aspects of information system security. Real-world business cases supplement and concretely illustrate the concepts studied. Mastering the terminology specific to information system security is a key objective of the course.

This course is structured into four hands-on modules. Three modules are dedicated to using and developing different types of models typically used by Earth Scientists. Many models rely on solving Partial Differential Equations (PDE). They either represent an efficient simplification thereof, e.g., in the form of box modelling (module 2 or directly write and solve (un)coupled sets of PDE’s (module 3). A new branch of modelling constitutes data-driven modelling, which exploits artificial intelligence to build models based on observations (module 2). These first three modules integrate the learning of more advanced programming with acquiring the basics of different methods used for modelling. The last module focuses on the process of modelling itself, such that you can translate Earth Science problems into effective modelling strategies. Together these modules complete the programming, modelling and data skill learning lines. Participants develop and write their own box model with a complete numerical model, which quantifies to what depth the water level would fall if the Mediterranean Sea was disconnected from the Atlantic Ocean. The course teaches the development and uses data-driven models. Learn the basics of different machine learning models, including how neural networks, random forests, and long short-term memory algorithms work. Train models using hydrological data and optimize their performance on different data sets. Finally, you build a machine learning model from scratch to simulate a new hydrological data set. Participants also develop or program various codes from scratch, starting from physical conservation laws to writing codes to simulate 1D and 2D flow; heat, chemical and pressure diffusion; and coupled momentum and continuity equations with variable viscosity. Developments are based on a staggered grid, finite difference formulation, which facilitates understanding of numerical mathematics for Earth Science students. Through gradually increasing code complexity students are introduced to spatial and temporal discretization, initial and boundary conditions, and basic numerical solvers. Finally, learnings is applied to perform a numerical modelling research project. The thermomechanical code used for this module is an extension of the building blocks you have programmed yourself in the PDE-module. You can choose to apply this code to simulate subduction over millions of years to understand earthquake sizes, flow of a mountain glacier, or tsunami propagation over a lake. Students are tasked to formulate a research question in one of these Earth Science domains based on literature and develop and execute a modelling strategy to answer it. Execute your research project step-by-step accumulating into a poster presentation. Assumed previous knowledge includes GEO1-1135, GEO2-1230 and GEO2-1301. 

This course explores the foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. It focuses on critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. This course also delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership.

The curriculum and format are designed specifically for students who have not previously taken statistics or computer science courses. Students with some prior experience in either statistics or computing are welcome to enroll and often find that this course offers a new perspective that blends computational and inferential thinking. Students who have taken several statistics or computer science courses should instead take a more advanced course.

The course introduces and explores technologies relating to high performance computing, and it offers practical hands-on use of and experience with said technologies. Students learn how to develop fast, efficient applications on the very latest advanced processors, including many-core CPUs and GPUs. Students also have the opportunity to integrate content from other courses, for example implementing high performance parallel versions of algorithms previously encountered. Students are exposed to the underlying trends in computer hardware that are driving development towards massive parallelism in hardware and software. They employ widely used parallel programming languages and tools, such as OpenMP, MPI, OpenCL, debuggers and profilers, all in the context of a real supercomputer environment: the university’s Blue Crystal cluster.

Machine learning tools are widely used across various aspects of contemporary society. A solid understanding of the foundations that support these tools is essential for developing new methods and assessing existing ones. This course introduces the mathematical and statistical theory behind modern machine learning methods. The course explores the why and how of machine learning methods from a theoretical perspective considering generalization, regularization, and optimization. Key techniques including kernel-based methods, tree-based methods, and neural networks are discussed, along with recent developments.

This course covers state-of-the-art mobile computing technologies and the development of application software for terminal devices. Due to recent advances in computing technology and wireless communication, multimedia information can now be processed "anytime, anywhere." These mobile computing systems consist of terminals (smartphones, tablet computers, etc.), wired and wireless networks, operating systems/middleware, and the integration of all these enables effective application services. Mobile computing is a key technology based on distributed computing and embedded system technologies, and is applied in ubiquitous systems, mobile commerce, mobile phones, and more. 

Topics include Life-Immersive mobile computing, Understanding behavior and contexts, Mobile data, Edge AI systems, AR and VR systems, Efficient ML models (pruning, quantization, neural architecture search, on-device training, knowledge distillation, application-specific optimizations). 

The evaluation will be based on four assignments, one mini-project, and the final exam. Students should be aware that there will not be a term project to design and develop a new mobile application. 

The course offers students a strong foundation in the theoretical side of computer science. The course explores the skills involved in dealing with abstract systems and constructing proofs, through both teaching and exercises. Students learn to construct rigorous arguments about properties of the Lambda Calculus, construct reduction sequences according to particular reduction orders, and understand the relationship between the Lambda Calculus and other notions of computability.

The course focuses on formal descriptions (models) of computational behavior. Students learn about the operational semantics (formal description) of a simple 'WHILE' programming language; the operational semantics of other styles of real-world languages, such as Java and Haskell; and equivalent definitions of algorithm, initiated in the 1930s and providing the foundations for programming languages and computation.

This course introduces a range of advanced topics in computer architecture, focusing mainly on high performance processors but including recent or hot topics (e.g., low-power design, wide vector units etc.). Students learn how various design decisions can improve quality (according to a metric such as performance, area or power consumption) which extends the output of previous units that focus mainly on functionality.

This course focuses on the theory and practice of concurrent programming, in order to familiarize students with a range of modern concurrent programming models, and with the challenges of correct concurrent programming. It is divided into two parts: a largely practical part that gives an overview of shared memory concurrency in modern programming languages, and a more theoretical part that treats shared memory concurrency from the ground up, covering low-level topics such as hardware memory models, primitives for shared-memory concurrency, building up towards higher-level synchronization techniques such as locks, mutexes and transactional memory. Throughout the course, a main objective is to reinforce correct concurrent programming through the use of tools, formal specification and verification techniques.

Machine learning is a fast-growing field and plays a central role in the era of AI and big data with many applications in prediction and data analysis. In particular, optimization formulations and algorithms are key to analysis and design of efficient methods in machine learning problems. This course focuses on the optimization models arising from various machine learning problems and introduce efficient algorithms for solving these models. The course covers basic convex analysis, model formulation, and fundamental (stochastic) first-order methods, supplemented with recent developments in the related literature.