Computer Science

This course offers a study of basic Machine Learning techniques, when to use Machine Learning on real problems, how to determine which technique is appropriate for each problem, and to apply the techniques in a practical way to real problems. Topics include: learning decision trees and rules; methodological aspects; learning regression trees and rules; ensembles of learning methods; frequent itemsets and association rules; reinforcement learning; relational learning.

The course aims to find solutions to problems using computer languages. Students learn how to solve a problem, and how to design and implement programming, including the implementation. The lectures use the 'C' language. 

'C++', 'Java', 'Python' and 'JavaScript' are introduced in the course. 

The course teaches students a thorough understanding of high-performance and energy-efficient computer architecture. Students learn principles and techniques for evaluating architectural proposals, explore how knowledge of computer architecture informs software performance engineering, and gain a deep understanding of topical trends in advanced computer architecture, compiler design, operating systems, and parallel processing

This lab course (Praktikum) trains in video encoding and transmission over communication networks. A particular focus will be on wireless and mobile networks, which are becoming increasingly important. After a successful completion the students are capable of encoding video clips, assessing the video quality using objective video quality metrics, and streaming the video. The students will further acquire the basics in the field of wireless communication - interference, broadcast communication medium, rate and power control. They will build up technical expertise on MAC and routing protocol behaviour in wireless mesh networking environments through various experiment set-up and performance evaluations.

In this course, you will create a graphical action game in Python. In the process, you will learn fundamental concepts and tools that programmers use. The course will guide you step by step from a first prototype to a working game. By the end of the course, you will deploy your game to a live website. No previous programming knowledge is required.

In this course, the fundamentals of Python are covered, with a special focus on the skills necessary for in-depth data analyses and data visualization. These two skills are fundamental in a wide range of disciplines, including but not limited to STEM (Sciences, Technology, Engineering and Mathematics) and Humanities fields of study. This course will cover the following: data types and compound data structures, conditional statements and loops, Python functions, importing, exporting and analyzing different types of data using pandas, visualizing data using Matplotlib and Seaborn, and developing interactive plots with Plotly. At the end of the two weeks course, students will work and present a final personal data analytics and visualization project.

The course covers several current and advanced topics in optimization, with an emphasis on efficient algorithms for solving large scale data-driven inference problems. Topics include first and second order methods, stochastic gradient type approaches and duality principles. Many relevant examples in statistical learning and machine learning are covered in detail. The algorithms uses the Python programming language. The course requires students to take prerequisites.

This data science course introduces essential techniques and tools used in Data Science. Each week the course covers a unique topic, starting from the very basics of the Data Science pipeline to advanced topics like Neural Networks and Time-Series Analysis, all explained using a sophisticated slides and easy-to-understand Python codes.
 

For this semester, the course will use a single comprehensive dataset that could cover all of the topics, to make it easier for students to understand the concepts of data science, without spending too much time understanding the dataset.
 

By the end of this course, students are expected to:

1.  Understand the Data Science Pipeline.
2. Apply various machine learning techniques.
3. Evaluate model performance and fine-tune hyperparameters.
4. Understand and apply Neural Networks, Text Mining, and Time-Series Analysis.
5. Translate theory into practice using Python.

Motion planning is a fundamental building block for autonomous systems, with applications in robotics, industrial automation, and autonomous driving. After completion of the course, students will have a detailed understanding of: Formalization of geometric, kinodynamic, and optimal motion planning; Sampling-based approaches: Rapidly-exploring random trees (RRT), probabilistic roadmaps (PRM), and variants; Search-based approaches: State-lattice based A* and variants; Optimization-based approaches: Differential Flatness and Sequential convex programming (SCP); The theoretical properties relevant to these algorithms (completeness, optimality, and complexity). Students will be able to: Decide (theoretically and empirically) which algorithm(s) to use for a given problem; Implement (basic versions) of the algorithms themselves; • Use current academic and industrial tools such as the Open Motion Planning Library (OMPL).

It provides a unified perspective on motion planning and includes topics from different research and industry communities. The goal is not only to learn the foundations and theory of currently used approaches, but also to be able to pick and compare the different methods for specific motion planning needs. An important emphasis is the consideration of both geometric and kinodynamic motion planning for the major algorithm types.

This course explores the truthfulness of AI, as a non-reliable source of information, from a linguistic angle. On the premise that AI-tools are increasingly used to provide “information” in professional and private settings, but in reality are producing ‘hallucinations’, false information, the course compares the logic and architecture behind large language models (LLM) used in AI-tools with the logic and architecture behind human cognition (including the capacity for language). It also delves into several aspects of human language that contribute to our inclination to take AI-generated output at face value.