Computer Science

This course develops foundational computing skills in Python and sharpens these skills through practice with exploration and problem-solving within the contexts of Applied, Pure, and Statistical Mathematics.

This is an interdisciplinary, project‑based course designed to introduce the principles, methods, and communication practices of modern scientific research. Through a flipped‑classroom approach, the course actively explores how different disciplines—such as biology, informatics, mathematics, physics, chemistry, and computer science—intersect to address complex scientific questions. Throughout the course, students work in small subgroups to build and communicate a scientific project. They learn how to identify and evaluate scientific literature, analyze research methodologies across fields, and critically assess the validity, reproducibility, and interpretation of results. Students develop strong skills in teamwork, scientific reasoning, and oral communication as they prepare an interdisciplinary presentation aimed at both specialists and non‑specialists. A major component of the course is the construction of a final oral presentation based on recent scientific publications. Students progressively refine their project through guided tutorials led by instructors from multiple disciplines. They also practice writing concise research abstracts, critically reading scientific articles, and using research tools such as PubMed and AI‑assisted platforms—while assessing their benefits and limitations. By the end of the course, students gain practical experience in the entire scientific communication pipeline: exploring a topic, building a multidisciplinary understanding of its methods, and presenting their findings clearly and rigorously to a diverse scientific audience.

This course introduces unsupervised learning and clustering algorithms, before exploring generative adversarial networks and deep generative models. It examines self-supervised learning, anomaly detection, flow-based models, and unsupervised representation learning. The final part of the course focuses on clustering in high-dimensional spaces, semi-supervised learning, energy-based models, and unsupervised learning for reinforcement. This intensive course offers theoretical understanding and practical experience with a focus throughout on real-world applications of deep unsupervised learning across various domains, offering career skills as well as excellent foundations for future research.

This course covers how modern computers operate and how to program them efficiently by making optimal use of their capabilities. The lectures introduce the internal functioning of computers through a progressive approach to fundamental concepts, organized into three main stages. First, it introduces machine‑level programming, including binary and hexadecimal arithmetic (signed and unsigned), and the use of registers, memory, and the stack. The second stage focuses on digital electronics, covering Boolean algebra and logic functions, as well as the design of memory circuits and data paths. The final stage examines processor design, including instruction encoding and the wiring of the processor data path. Tutorial sessions deepen understanding through paper‑based problem solving, emphasizing conceptual reasoning without the need for a physical computer. Practical sessions provide hands‑on experience using educational simulators that correspond to the three phases of the course: execution of simplified machine‑language programs, design of digital electronic circuits, and processor wiring simulation, allowing students to modify and experiment with instruction sets.

In his course, student learn about reinforcement learning through an integrated approach that traces core algorithmic concepts from their theoretical foundations to state-of-the-art implementations. It covers tabular and deep reinforcement learning topics and couples classical and modern approaches to provide students with a unified understanding of how RL algorithms have evolved and are applied today.