We study emerging wireless networks such as sensor or ad hoc networks. Our goal is to get a deeper understanding of these self-organizing networks, from theory to applications. Topics include: Applications, Modeling, Media Access, Topology Control, Clustering, Time Synchronization, Localization & Positioning, Routing & Data Gathering.

We learn: algorithmic principles, dynamic and linear programming, complexity, P vs. NP, approximation, reductions, cryptography, zero-knowledge proofs, relational databases, SQL, machine learning, regression, gradient descent, decision trees, deep neural networks, universal approximation, advanced layers and architectures, reinforcement learning, Turing machines, computability, and more.

This is an introductory course on computer networks (textbook by Tanenbaum), from basic protocols, to standard network layer. The course also covers the basics of distributed systems from RPC to transactions, consistency (consensus, 2PC), as well as an introduction to web services. Network programming at different levels (from sockets to RMI to messages queues) is an integral part of the course.

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This course is about real computer systems, and the principles on which they are designed and built. We cover both modern OSes and the large-scale distributed systems that power today's online services. We illustrate the ideas with real-world examples, but emphasize common theoretical results, practical tradeoffs, and design principles that apply across many different scales and technologies.

DLT is emerging for a disruption of our current financial infrastructure. As such, Blockchain Finance seeks to combine open-source, peer to peer building blocks into sophisticated products using blockchain technology, seeking to disintermediate and decentralize the traditional financial service industry. This lecture will combine insights on DLT with recent applications from finance.

Introduction to discrete event systems. We study popular models of discrete event systems, learn how to analyze them, as well as verify them. Topics include: Automata and Languages, Specification Models, Stochastic Discrete Event Systems (Markov Chains), Verification.

This course introduces the fundamentals of distributed systems. We study different protocols and algorithms that allow for fault-tolerant operation, and discuss practical systems that implement these techniques.

This course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including as well wireless networks, ad-hoc networks, and distributed application on mobile phones.

This course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including as well wireless networks, ad-hoc networks, and distributed application on PDAs.

This course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. Projects are available in a wide range of areas: from web services to ubiquitous computing as well as wireless networks. The objective of the project is for the students to gain hands-on experience on tools and problems in the context of a real team project.

This lab offers hands-on deep learning exercises using PyTorch, covering computer vision, audio processing, graph neural networks, natural language processing, reinforcement learning, and representation learning. The material is organized into six topics, each spanning two weeks.

Technological advances, digitization and the ability to store and process vast amounts of data has changed the landscape of financial services in recent years. This course will unpack these innovations and technologies underlying these transformations and will reflect on the impacts on the financial markets.

The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work.

We study the fundamental issues underlying the design of distributed systems: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques.

In this seminar participating students present and discuss recent research papers in the area of deep neural networks.

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The course discusses algorithmic issues related to the Web, employing interesting algorithmic and mathematical techniques for modeling and analyzing various Web related problems w.r.t. network structure (small world, hotlink assignment, page ranking), basics of game theory, selfish agents, auctions, distributed selfish packet routing and load balancing, and on-line control in some generality.