Computer Science

This course covers methods and techniques to summarize and communicate the underlying patterns of different types of data. Students practice preprocessing (Python) and various data visualization tools, working on projects with real-world data. Topics include overview of data visualization, graph visual perception and design principles, data types and visualization techniques, basic and advanced charts and graphs, effective communication, time series data visualization and interactive visualizations, statistical visualizations, network data, machine learning, and data visualization trends.  

This course introduces mathematical tools to evaluate software products and algorithms, examining principal knowledge of data structures in order to represent real world problems with computational algorithms (with abstract data types).  

Topics include linear lists, queues, trees, arrays, and hash tables. The course also investigates the principles of software engineering and addresses how to best utilize the acquired principles in designing and realizing software problems through hands-on experience. 

This course examines Big Data computing systems and programming models. It covers the architecture and components of Hadoop and Spark, data processing with Spark, and advanced topics such as Spark Streaming, graph processing, and machine learning. Students will learn to develop operational and programming tools for data collection, serialization, migration, and workflow coordination in Big Data pipelines. 

This course cultivates a deep understanding of data augmentation techniques and robust machine learning principles and the ability to apply them to real-world problems.  

Students will implement various data augmentation techniques using programming languages and machine learning libraries and develop problem-solving skills to diagnose and address the performance degradation caused by noisy labels and imbalanced data. Additionally, students will master the use of cross-validation and performance metrics to effectively evaluate models, and learn methods to interpret and explain model predictions, ensuring the development of transparent and trustworthy machine learning applications. The course also emphasizes the ethical aspects of data augmentation and robust machine learning, fostering the ability to implement ethical practices that ensure responsible use of technology. Students will nurture a research-oriented mindset and enhance their collaboration skills through team projects and group discussions, promoting the exchange of ideas. 

This course examines to the computational modelling of natural language, including algorithms, formalisms, and applications. It covers computational morphology, language modelling, syntactic parsing, lexical and compositional semantics, discourse analysis, automatic summarization, machine translation, speech processing, and machine learning. 

An overview is given of a general communication link consisting of the three parts: transmitter, communication channel, and receiver. Examples of digital communication methods are introduced for realistic bit rates and noise levels. Some of the following applications are considered in the course: Mobile digital telephony (3G, EDGE, GSM), WLAN, modem, ADSL, digital TV, Bluetooth, navigation (GPS), surveillance systems.

This course provides a basic and broad introduction to the representation, analysis, and processing of sampled data. The course introduces statistical analysis, mathematical modeling, machine learning, and visualization for experimental data. Examples are taken from real-world problems, such as analysis of internet traffic, language technology, digital sound, and image processing.

This course introduces mathematical and programming skills that are employed by researchers in the Molecular and Biophysical Life Sciences to analyze and integrate data and to understand the physics of living systems. The course is divided into two parts that run in parallel. The mathematics part of the course consists of nine lectures that cover: basic algebra, goniometry, differentiation and integration (including functions of multiple variables), limits, (partial) differential equations (first order and second order), Taylor expansion, basic probability theory and statistics and vectors (including dot product and cross product). Each lecture is followed by a supervised practical session. The programming part consists of six lectures that introduce the basics of programming by discussing the modulare structure of programs (modules, functions, loops), different data types and variables, as well as good practices. For some calculations of the mathematics part of the course it is explained how to perform those calculations using Python. After each lecture, students work individually on a series of practical coding assignments that familiarize them with the basics of programming in Python during supervised tutorials, where regular instruction and feedback is provided.

The cooperation of people from different disciplinary backgrounds is becoming increasingly important in developing knowledge and solutions in a complex world. This course explores how knowledge is created in and across disciplines, specifically by examining intersection between Computer Science and the disciplines of the Humanities commonly known as the Digital Humanities. It illustrates the potential benefits of and challenges to these interactions, by examining both emerging digital technologies and the traditional roots of cultural production such as language, historical records and institutions, and the arts. 

This course covers algorithm expression methods, functions and processing processes, analysis of difficulty, techniques for designing efficient algorithms, applications, and categorizes and utilizes previously developed algorithms by topic.