Search result: Catalogue data in Spring Semester 2020
For the Master's degree in Applied Mathematics the following additional condition (not manifest in myStudies) must be obeyed: At least 15 of the required 28 credits from core courses and electives must be acquired in areas of applied mathematics and further application-oriented fields.
| Electives: Applied Mathematics and Further Application-Oriented Fields|
| Selection: Theoretical Computer Science, Discrete Mathematics|
In the Master's programme in Mathematics 401-3052-05L Graph Theory is eligible as an elective course, but only if 401-3052-10L Graph Theory isn't recognised for credits (neither in the Bachelor's nor in the Master's programme). For the category assignment take contact with the Study Administration Office (www.math.ethz.ch/studiensekretariat) after having received the credits.
|252-0408-00L||Cryptographic Protocols||W||6 credits||2V + 2U + 1A||M. Hirt, U. Maurer|
|Abstract||The course presents a selection of hot research topics in cryptography. The choice of topics varies and may include provable security, interactive proofs, zero-knowledge protocols, secret sharing, secure multi-party computation, e-voting, etc.|
|Objective||Indroduction to a very active research area with many gems and paradoxical|
results. Spark interest in fundamental problems.
|Content||The course presents a selection of hot research topics in cryptography. The choice of topics varies and may include provable security, interactive proofs, zero-knowledge protocols, secret sharing, secure multi-party computation, e-voting, etc.|
|Lecture notes||the lecture notes are in German, but they are not required as the entire|
course material is documented also in other course material (in english).
|Prerequisites / Notice||A basic understanding of fundamental cryptographic concepts |
(as taught for example in the course Information Security or
in the course Cryptography Foundations) is useful, but not required.
|263-4660-00L||Applied Cryptography |
Number of participants limited to 150.
|W||8 credits||3V + 2U + 2P||K. Paterson|
|Abstract||This course will introduce the basic primitives of cryptography, using rigorous syntax and game-based security definitions. The course will show how these primitives can be combined to build cryptographic protocols and systems.|
|Objective||The goal of the course is to put students' understanding of cryptography on sound foundations, to enable them to start to build well-designed cryptographic systems, and to expose them to some of the pitfalls that arise when doing so.|
|Content||Basic symmetric primitives (block ciphers, modes, hash functions); generic composition; AEAD; basic secure channels; basic public key primitives (encryption,signature, DH key exchange); ECC; randomness; applications.|
|Literature||Textbook: Boneh and Shoup, “A Graduate Course in Applied Cryptography”, https://crypto.stanford.edu/~dabo/cryptobook/BonehShoup_0_4.pdf.|
|Prerequisites / Notice||Ideally, students will have taken the D-INFK Bachelors course “Information Security" or an equivalent course at Bachelors level.|
|263-4400-00L||Advanced Graph Algorithms and Optimization |
Number of participants limited to 30.
|W||5 credits||3G + 1A||R. Kyng|
|Abstract||This course will cover a number of advanced topics in optimization and graph algorithms.|
|Objective||The course will take students on a deep dive into modern approaches to|
graph algorithms using convex optimization techniques.
By studying convex optimization through the lens of graph algorithms,
students should develop a deeper understanding of fundamental
phenomena in optimization.
The course will cover some traditional discrete approaches to various graph
problems, especially flow problems, and then contrast these approaches
with modern, asymptotically faster methods based on combining convex
optimization with spectral and combinatorial graph theory.
|Content||Students should leave the course understanding key|
concepts in optimization such as first and second-order optimization,
convex duality, multiplicative weights and dual-based methods,
acceleration, preconditioning, and non-Euclidean optimization.
Students will also be familiarized with central techniques in the
development of graph algorithms in the past 15 years, including graph
decomposition techniques, sparsification, oblivious routing, and
spectral and combinatorial preconditioning.
|Prerequisites / Notice||This course is targeted toward masters and doctoral students with an|
interest in theoretical computer science.
Students should be comfortable with design and analysis of algorithms, probability, and linear algebra.
Having passed the course Algorithms, Probability, and Computing (APC) is highly recommended, but not formally required. If you are not
sure whether you're ready for this class or not, please consult the
|263-4507-00L||Advances in Distributed Graph Algorithms|
Does not take place this semester.
|W||6 credits||3V + 1U + 1A||M. Ghaffari|
|Abstract||How can a network of computers solve the graph problems needed for running that network?|
|Objective||This course will familiarize the students with the algorithmic tools and techniques in local distributed graph algorithms, and overview the recent highlights in the field. This will also prepare the students for independent research at the frontier of this area.|
This is a special‐topics course in algorithm design. It should be accessible to any student with sufficient theoretical/algorithmic background. In particular, it assumes no familiarity with distributed computing. We only expect that the students are comfortable with the basics of algorithm design and analysis, as well as probability theory. It is possible to take this course simultaneously with the course “Principles of Distributed Computing”. If you are not sure whether you are ready for this class or not, please consult the instructor.
|Content||How can a network of computers solve the graph problems needed for running that network?|
Answering this and similar questions is the underlying motivation of the area of Distributed Graph Algorithms. The area focuses on the foundational algorithmic aspects in these questions and provides methods for various distributed systems --- e.g., the Internet, a wireless network, a multi-processor computer, etc --- to solve computational problems that can be abstracted as graph problems. For instance, think about shortest path computation in routing, or about coloring and independent set computation in contention resolution.
Over the past decade, we have witnessed a renaissance in the area of Distributed Graph Algorithms, with tremendous progress in many directions and solutions for a number of decades-old central problems. This course overviews the highlights of these results. The course will mainly focus on one half of the field, which revolves around locality and local problems. The other half, which relates to the issue of congestion and dealing with limited bandwidth in global problems, will not be addressed in this offering of the course.
The course will cover a sampling of the recent developments (and open questions) at the frontier of research of distributed graph algorithms. The material will be based on a compilation of recent papers in this area, which will be provided throughout the semester. The tentative list of topics includes:
- The shattering technique for local graph problems and its necessity
- Lovasz Local Lemma algorithms, their distributed variants, and distributed applications
- Distributed Derandomization
- Distributed Lower bounds
- Graph Coloring
- Complexity Hierarchy and Gaps
- Primal-Dual Techniques
|Prerequisites / Notice||The class assumes no knowledge in distributed algorithms/computing. Our only prerequisite is the undergraduate class Algorithms, Probability, and Computing (APC) or any other course that can be seen as the equivalent. In particular, much of what we will discuss uses randomized algorithms and therefore, we will assume that the students are familiar with the tools and techniques in randomized algorithms and analysis (to the extent covered in the APC class).|
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