Search result: Catalogue data in Spring Semester 2023

Computer Science Master Information
Minors
Minor in Theoretical Computer Science
NumberTitleTypeECTSHoursLecturers
252-1424-00LModels of ComputationW6 credits2V + 2U + 1AM. Cook
AbstractThis course surveys many different models of computation: Turing Machines, Cellular Automata, Finite State Machines, Graph Automata, Circuits, Tilings, Lambda Calculus, Fractran, Chemical Reaction Networks, Hopfield Networks, String Rewriting Systems, Tag Systems, Diophantine Equations, Register Machines, Primitive Recursive Functions, and more.
Learning objectiveThe goal of this course is to become acquainted with a wide variety of models of computation, to understand how models help us to understand the modeled systems, and to be able to develop and analyze models appropriate for new systems.
ContentThis course surveys many different models of computation: Turing Machines, Cellular Automata, Finite State Machines, Graph Automata, Circuits, Tilings, Lambda Calculus, Fractran, Chemical Reaction Networks, Hopfield Networks, String Rewriting Systems, Tag Systems, Diophantine Equations, Register Machines, Primitive Recursive Functions, and more.
261-5110-00LOptimization for Data Science Information W10 credits3V + 2U + 4AB. Gärtner, N. He
AbstractThis course provides an in-depth theoretical treatment of optimization methods that are relevant in data science.
Learning objectiveUnderstanding the guarantees and limits of relevant optimization methods used in data science. Learning theoretical paradigms and techniques to deal with optimization problems arising in data science.
ContentThis course provides an in-depth theoretical treatment of classical and modern optimization methods that are relevant in data science.

After a general discussion about the role that optimization has in the process of learning from data, we give an introduction to the theory of (convex) optimization. Based on this, we present and analyze algorithms in the following four categories: first-order methods (gradient and coordinate descent, Frank-Wolfe, subgradient and mirror descent, stochastic and incremental gradient methods); second-order methods (Newton and quasi Newton methods); non-convexity (local convergence, provable global convergence, cone programming, convex relaxations); min-max optimization (extragradient methods).

The emphasis is on the motivations and design principles behind the algorithms, on provable performance bounds, and on the mathematical tools and techniques to prove them. The goal is to equip students with a fundamental understanding about why optimization algorithms work, and what their limits are. This understanding will be of help in selecting suitable algorithms in a given application, but providing concrete practical guidance is not our focus.
Prerequisites / NoticeA solid background in analysis and linear algebra; some background in theoretical computer science (computational complexity, analysis of algorithms); the ability to understand and write mathematical proofs.
263-4400-00LAdvanced Graph Algorithms and Optimization Information W10 credits3V + 3U + 3AR. Kyng, M. Probst
AbstractThis course will cover a number of advanced topics in optimization and graph algorithms.
Learning objectiveThe 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.
ContentStudents 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 / NoticeThis 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
instructor.
263-4508-00LAlgorithmic Foundations of Data Science Information W10 credits3V + 2U + 4AD. Steurer
AbstractThis course provides rigorous theoretical foundations for the design and mathematical analysis of efficient algorithms that can solve fundamental tasks relevant to data science.
Learning objectiveWe consider various statistical models for basic data-analytical tasks, e.g., (sparse) linear regression, principal component analysis, matrix completion, community detection, and clustering.

Our goal is to design efficient (polynomial-time) algorithms that achieve the strongest possible (statistical) guarantees for these models.

Toward this goal we learn about a wide range of mathematical techniques from convex optimization, linear algebra (especially, spectral theory and tensors), and high-dimensional statistics.

We also incorporate adversarial (worst-case) components into our models as a way to reason about robustness guarantees for the algorithms we design.
ContentStrengths and limitations of efficient algorithms in (robust) statistical models for the following (tentative) list of data analysis tasks:

- (sparse) linear regression
- principal component analysis and matrix completion
- clustering and Gaussian mixture models
- community detection
Lecture notesTo be provided during the semester
LiteratureHigh-Dimensional Statistics
A Non-Asymptotic Viewpoint
by Martin J. Wainwright
Prerequisites / NoticeMathematical and algorithmic maturity at least at the level of the course "Algorithms, Probability, and Computing".

Important: Optimization for Data Science 2018--2021
This course was created after a reorganization of the course "Optimization for Data Science" (ODS).
A significant portion of the material for this course has previously been taught as part of ODS.
Consequently, it is not possible to earn credit points for both this course and ODS as offered in 2018--2021.
This restriction does not apply to ODS offered in 2022 or afterwards and you can earn credit points for both courses in this case.
263-4509-00LComplex Network ModelsW5 credits2V + 2AJ. Lengler
AbstractComplex network models are random graphs that feature one or several properties observed in real-world networks (e.g., social networks, internet graph, www). Depending on the application, different properties are relevant, and different complex network models are useful. This course gives an overview over some relevant models and the properties they do and do not cover.
Learning objectiveThe students get familiar with a portfolio of network models, and they know their features and shortcomings. For a given application, they can identify relevant properties for this applications and can select an appropriate network model.
ContentNetwork models: Erdös-Renyi random graphs, Chung-Lu graphs, configuration model, Kleinberg model, geometric inhomogeneous random graphs
Properties: degree distribution, structure of giant and smaller components, clustering coefficient, small-world properties, community structures, weak ties
Lecture notesThe script is available in moodle or at https://as.inf.ethz.ch/people/members/lenglerj/CompNetScript.pdf

It will be updated during the semester.
LiteratureLatora, Nikosia, Russo: "Complex Networks: Principles, Methods and Applications"
van der Hofstad: "Random Graphs and Complex Networks. Volume 1"
Prerequisites / NoticeThe students must be familiar with the basics of graph theory and of probability theory (e.g. linearity of expectation, inequalities of Markov, Chebyshev, Chernoff). The course "Randomized Algorithms and Probabilistic Methods" is helpful, but not required.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
263-4510-00LIntroduction to Topological Data Analysis Information W8 credits3V + 2U + 2AP. Schnider
AbstractTopological Data Analysis (TDA) is a relatively new subfield of computer sciences, which uses techniques from algebraic topology and computational geometry and topology to analyze and quantify the shape of data. This course will introduce the theoretical foundations of TDA.
Learning objectiveThe goal is to make students familiar with the fundamental concepts, techniques and results in TDA. At the end of the course, students should be able to read and understand current research papers and have the necessary background knowledge to apply methods from TDA to other projects.
ContentMathematical background (Topology, Simplicial complexes, Homology), Persistent Homology, Complexes on point clouds (Čech complexes, Vietoris-Rips complexes, Delaunay complexes, Witness complexes), the TDA pipeline, Reeb Graphs, Mapper
LiteratureMain reference:

Tamal K. Dey, Yusu Wang: Computational Topology for Data Analysis, 2021
https://www.cs.purdue.edu/homes/tamaldey/book/CTDAbook/CTDAbook.html


Other references:

Herbert Edelsbrunner, John Harer: Computational Topology: An Introduction, American Mathematical Society, 2010
https://bookstore.ams.org/mbk-69

Gunnar Carlsson, Mikael Vejdemo-Johansson: Topological Data Analysis with Applications, Cambridge University Press, 2021
Link

Robert Ghrist: Elementary Applied Topology, 2014
https://www2.math.upenn.edu/~ghrist/notes.html

Allen Hatcher: Algebraic Topology, Cambridge University Press, 2002
https://pi.math.cornell.edu/~hatcher/AT/ATpage.html
Prerequisites / NoticeThe course assumes knowledge of discrete mathematics, algorithms and data structures and linear algebra, as supplied in the first semesters of Bachelor Studies at ETH.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Problem-solvingassessed
Project Managementfostered
Social CompetenciesCommunicationassessed
Cooperation and Teamworkfostered
Self-presentation and Social Influence fostered
Personal CompetenciesCreative Thinkingfostered
263-4656-00LDigital Signatures Information W5 credits2V + 2AD. Hofheinz
AbstractDigital signatures as one central cryptographic building block. Different security goals and security definitions for digital signatures, followed by a variety of popular and fundamental signature schemes with their security analyses.
Learning objectiveThe student knows a variety of techniques to construct and analyze the security of digital signature schemes. This includes modularity as a central tool of constructing secure schemes, and reductions as a central tool to proving the security of schemes.
ContentWe will start with several definitions of security for signature schemes, and investigate the relations among them. We will proceed to generic (but inefficient) constructions of secure signatures, and then move on to a number of efficient schemes based on concrete computational hardness assumptions. On the way, we will get to know paradigms such as hash-then-sign, one-time signatures, and chameleon hashing as central tools to construct secure signatures.
LiteratureJonathan Katz, "Digital Signatures."
Prerequisites / NoticeIdeally, students will have taken the D-INFK Bachelors course "Information Security" or an equivalent course at Bachelors level.
272-0300-00LAlgorithmics for Hard Problems Information
This course d o e s n o t include the Mentored Work Specialised Courses with an Educational Focus in Computer Science A.
W5 credits2V + 1U + 1AH.‑J. Böckenhauer, D. Komm
AbstractThis course unit looks into algorithmic approaches to the solving of hard problems, particularly with moderately exponential-time algorithms and parameterized algorithms.

The seminar is accompanied by a comprehensive reflection upon the significance of the approaches presented for computer science tuition at high schools.
Learning objectiveTo systematically acquire an overview of the methods for solving hard problems. To get deeper knowledge of exact and parameterized algorithms.
ContentFirst, the concept of hardness of computation is introduced (repeated for the computer science students). Then some methods for solving hard problems are treated in a systematic way. For each algorithm design method, it is discussed what guarantees it can give and how we pay for the improved efficiency. A special focus lies on moderately exponential-time algorithms and parameterized algorithms.
Lecture notesUnterlagen und Folien werden zur Verfügung gestellt.
LiteratureJ. Hromkovic: Algorithmics for Hard Problems, Springer 2004.

R. Niedermeier: Invitation to Fixed-Parameter Algorithms, 2006.

M. Cygan et al.: Parameterized Algorithms, 2015.

F. Fomin et al.: Kernelization, 2019.

F. Fomin, D. Kratsch: Exact Exponential Algorithms, 2010.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Problem-solvingassessed
Social CompetenciesCommunicationfostered
Cooperation and Teamworkfostered
Self-presentation and Social Influence fostered
Personal CompetenciesCreative Thinkingassessed
Critical Thinkingassessed
Self-awareness and Self-reflection fostered
Self-direction and Self-management fostered
272-0302-00LApproximation and Online Algorithms Information
Does not take place this semester.
W5 credits2V + 1U + 1AD. Komm
AbstractThis lecture deals with approximative algorithms for hard optimization problems and algorithmic approaches for solving online problems as well as the limits of these approaches.
Learning objectiveGet a systematic overview of different methods for designing approximative algorithms for hard optimization problems and online problems. Get to know methods for showing the limitations of these approaches.
ContentApproximation algorithms are one of the most succesful techniques to attack hard optimization problems. Here, we study the so-called approximation ratio, i.e., the ratio of the cost of the computed approximating solution and an optimal one (which is not computable efficiently).
For an online problem, the whole instance is not known in advance, but it arrives pieceweise and for every such piece a corresponding part of the definite output must be given. The quality of an algorithm for such an online problem is measured by the competitive ratio, i.e., the ratio of the cost of the computed solution and the cost of an optimal solution that could be given if the whole input was known in advance.

The contents of this lecture are
- the classification of optimization problems by the reachable approximation ratio,
- systematic methods to design approximation algorithms (e.g., greedy strategies, dynamic programming, linear programming relaxation),
- methods to show non-approximability,
- classic online problem like paging or scheduling problems and corresponding algorithms,
- randomized online algorithms,
- the design and analysis principles for online algorithms, and
- limits of the competitive ratio and the advice complexity as a way to do a deeper analysis of the complexity of online problems.
LiteratureThe lecture is based on the following books:

J. Hromkovic: Algorithmics for Hard Problems, Springer, 2004

D. Komm: An Introduction to Online Computation: Determinism, Randomization, Advice, Springer, 2016

Additional literature:

A. Borodin, R. El-Yaniv: Online Computation and Competitive Analysis, Cambridge University Press, 1998
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Problem-solvingassessed
Social CompetenciesCommunicationfostered
Cooperation and Teamworkfostered
Self-presentation and Social Influence fostered
Personal CompetenciesCreative Thinkingassessed
Critical Thinkingassessed
Self-awareness and Self-reflection fostered
Self-direction and Self-management fostered
401-3052-10LGraph TheoryW10 credits4V + 1UB. Sudakov
AbstractBasics, trees, Caley's formula, matrix tree theorem, connectivity, theorems of Mader and Menger, Eulerian graphs, Hamilton cycles, theorems of Dirac, Ore, Erdös-Chvatal, matchings, theorems of Hall, König, Tutte, planar graphs, Euler's formula, Kuratowski's theorem, graph colorings, Brooks' theorem, 5-colorings of planar graphs, list colorings, Vizing's theorem, Ramsey theory, Turán's theorem
Learning objectiveThe students will get an overview over the most fundamental questions concerning graph theory. We expect them to understand the proof techniques and to use them autonomously on related problems.
Lecture notesLecture will be only at the blackboard.
LiteratureWest, D.: "Introduction to Graph Theory"
Diestel, R.: "Graph Theory"

Further literature links will be provided in the lecture.
Prerequisites / NoticeStudents are expected to have a mathematical background and should be able to write rigorous proofs.
401-3902-21LNetwork & Integer Optimization: From Theory to ApplicationW6 credits3GR. Zenklusen
AbstractThis course covers various topics in Network and (Mixed-)Integer Optimization. It starts with a rigorous study of algorithmic techniques for some network optimization problems (with a focus on matching problems) and moves to key aspects of how to attack various optimization settings through well-designed (Mixed-)Integer Programming formulations.
Learning objectiveOur goal is for students to both get a good foundational understanding of some key network algorithms and also to learn how to effectively employ (Mixed-)Integer Programming formulations, techniques, and solvers, to tackle a wide range of discrete optimization problems.
ContentKey topics include:
- Matching problems;
- Integer Programming techniques and models;
- Extended formulations and strong problem formulations;
- Solver techniques for (Mixed-)Integer Programs;
- Decomposition approaches.
Literature- Bernhard Korte, Jens Vygen: Combinatorial Optimization. 6th edition, Springer, 2018.
- Alexander Schrijver: Combinatorial Optimization: Polyhedra and Efficiency. Springer, 2003. This work has 3 volumes.
- Vanderbeck François, Wolsey Laurence: Reformulations and Decomposition of Integer Programs. Chapter 13 in: 50 Years of Integer Programming 1958-2008. Springer, 2010.
- Alexander Schrijver: Theory of Linear and Integer Programming. John Wiley, 1986.
Prerequisites / NoticeSolid background in linear algebra. Preliminary knowledge of Linear Programming is ideal but not a strict requirement. Prior attendance of the course Linear & Combinatorial Optimization is a plus.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingassessed
Problem-solvingassessed
Social CompetenciesCommunicationassessed
Personal CompetenciesCreative Thinkingassessed
402-0448-01LQuantum Information Processing I: Concepts
This theory part QIP I together with the experimental part 402-0448-02L QIP II (both offered in the Spring Semester) combine to the core course in experimental physics "Quantum Information Processing" (totally 10 ECTS credits). This applies to the Master's degree programme in Physics.
W5 credits2V + 1UJ. Home
AbstractThe course covers the key concepts of quantum information processing, including quantum algorithms which give the quantum computer the power to compute problems outside the reach of any classical supercomputer.
Key concepts such as quantum error correction are discussed in detail. They provide fundamental insights into the nature of quantum states and measurements.
Learning objectiveBy the end of the course students are able to explain the basic mathematical formalism of quantum mechanics and apply them to quantum information processing problems. They are able to adapt and apply these concepts and methods to analyse and discuss quantum algorithms and other quantum information-processing protocols.
ContentThe topics covered in the course will include quantum circuits, gate decomposition and universal sets of gates, efficiency of quantum circuits, quantum algorithms (Shor, Grover, Deutsch-Josza,..), quantum error correction, fault-tolerant designs, and quantum simulation.
Lecture notesWill be provided.
LiteratureQuantum Computation and Quantum Information
Michael Nielsen and Isaac Chuang
Cambridge University Press
Prerequisites / NoticeA good understanding of finite dimensional linear algebra is recommended.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Interfocus Courses
NumberTitleTypeECTSHoursLecturers
263-0007-00LAdvanced Systems Lab Information Restricted registration - show details
Only for master students, otherwise a special permission by the study administration of D-INFK is required.
O8 credits3V + 2U + 2AM. Püschel
AbstractThis course introduces the student to the foundations and state-of-the-art techniques in developing high performance software for mathematical functionality occurring in various fields in computer science. The focus is on optimizing for a single core and includes optimizing for the memory hierarchy, for special instruction sets, and the possible use of automatic performance tuning.
Learning objectiveSoftware performance (i.e., runtime) arises through the complex interaction of algorithm, its implementation, the compiler used, and the microarchitecture the program is run on. The first goal of the course is to provide the student with an understanding of this "vertical" interaction, and hence software performance, for mathematical functionality. The second goal is to teach a systematic strategy how to use this knowledge to write fast software for numerical problems. This strategy will be trained in several homeworks and a semester-long group project.
ContentThe fast evolution and increasing complexity of computing platforms pose a major challenge for developers of high performance software for engineering, science, and consumer applications: it becomes increasingly harder to harness the available computing power. Straightforward implementations may lose as much as one or two orders of magnitude in performance. On the other hand, creating optimal implementations requires the developer to have an understanding of algorithms, capabilities and limitations of compilers, and the target platform's architecture and microarchitecture.

This interdisciplinary course introduces the student to the foundations and state-of-the-art techniques in high performance mathematical software development using important functionality such as matrix operations, transforms, filters, and others as examples. The course will explain how to optimize for the memory hierarchy, take advantage of special instruction sets, and other details of current processors that require optimization. The concept of automatic performance tuning is introduced. The focus is on optimization for a single core; thus, the course complements others on parallel and distributed computing.

Finally a general strategy for performance analysis and optimization is introduced that the students will apply in group projects that accompany the course.
Prerequisites / NoticeSolid knowledge of the C programming language and matrix algebra.
263-0008-00LComputational Intelligence Lab
Only for master students, otherwise a special permission by the study administration of D-INFK is required.
O8 credits2V + 2U + 3AT. Hofmann
AbstractThis laboratory course teaches fundamental concepts in computational science and machine learning with a special emphasis on matrix factorization and representation learning. The class covers techniques like dimension reduction, data clustering, sparse coding, and deep learning as well as a wide spectrum of related use cases and applications.
Learning objectiveStudents acquire fundamental theoretical concepts and methodologies from machine learning and how to apply these techniques to build intelligent systems that solve real-world problems. They learn to successfully develop solutions to application problems by following the key steps of modeling, algorithm design, implementation and experimental validation.

This lab course has a strong focus on practical assignments. Students work in groups of three to four people, to develop solutions to three application problems: 1. Collaborative filtering and recommender systems, 2. Text sentiment classification, and 3. Road segmentation in aerial imagery.

For each of these problems, students submit their solutions to an online evaluation and ranking system, and get feedback in terms of numerical accuracy and computational speed. In the final part of the course, students combine and extend one of their previous promising solutions, and write up their findings in an extended abstract in the style of a conference paper.

(Disclaimer: The offered projects may be subject to change from year to year.)
Contentsee course description
Elective Courses
Students can individually chose from the entire Master course offerings in the area of Computer Science (or a closely related field), from ETH Zurich, EPF Lausanne, the University of Zurich and - but only with the consent of the Director of Studies - from all other Swiss universities.
NumberTitleTypeECTSHoursLecturers
252-0820-00LInformation Technology in PracticeW5 credits2V + 1U + 1AM. Brandis
AbstractThe course is designed to provide students with an understanding of "real-life" computer science challenges in business settings and teach them how to address these.
Learning objectiveStudents will learn important considerations of companies when applying information technology in practice, including costs, economic value and risks of information technology use, or impact of information technology on business strategy and vice versa. They will get insight into how companies have used or are using information technology to be successful. Students will also learn how to assess information technology decisions from different viewpoints, including technical experts, IT managers, business users, and business top managers.

The course will equip participants to understand the role computer science and information technology plays in different companies and to contribute to respective decisions as they enter into practice.
ContentThe course consists of multiple lectures on economics of information technology, business and IT strategy, and how they are interlinked, and a set of relevant case studies. They address how companies become more successful using information technology, how bad information technology decisions can hurt them, and they look into a number of current challenges companies face regarding their information technology.

The cases are taken both from documented international case studies as well as from Swiss companies participating in the course.

The learned concepts will be applied in exercises, which form a key component of the course.
Prerequisites / NoticeThe course builds on the earlier "Case Studies from Practice" course, with a stronger focus on learning key concepts of information technology use in practice and applying them in exercises, and only a limited number of case studies.
The course prepares students for participation in the subsequent "Case Studies from Practice Seminar", which provides deeper insights into actual cases and how to solve them.
263-0600-00LResearch in Computer Science Information Restricted registration - show details W5 credits11AProfessors
AbstractIndependent project work under the supervision of a Computer Science Professor.
Learning objectiveProject done under supervision of a professor in the Department of Computer Science.
Prerequisites / NoticeOnly students who fulfill one of the following requirements are allowed to begin a research project:
a) 1 lab (interfocus course) and 1 core focus course
b) 2 core focus courses
c) 2 labs (interfocus courses)

A task description must be submitted to the Student Administration Office at the beginning of the work.
263-5055-00LTalent Kick: From Student to Entrepreneur Information Restricted registration - show details W3 credits2GV. Gropengiesser, A. Ilic
AbstractThe transfer of the latest research results into scalable start-ups creates the prerequisite forsuccessful innovations. An entrepreneurial spirit and mindset enables young leaders to navigate complex environments and bring their research into practice. Studies are the best time to develop an entrepreneurial mindset and explore the entrepreneurial career path.
Learning objectiveThis seminar helps aspiring student/research entrepreneurs to gain hands-on entrepreneurial experience on the path from research into practice.
The examples and cases will be primarily from software, AI, and other deep-tech ventures.

The seminar was created with the support of ETH AI Center and University of St. Gallen and received competitive funding from the ETH Board, Fondation Botnar, Gebert Rüf Foundation, as well as support from the ETH Foundation.
ContentAfter attending this course, students will be able to:
● Explain the importance and tools to form successful interdisciplinary teams
● Structure customer calls and sales pitchdecks
● Build their first prototypes and MVPs
● Find the right markets and customers to bring your research into practice
● Deal with complexity in bringing innovative / novel products into market
● Develop customer-centric business strategy
● Convince first supporters incl. Entrepreneurial mentors, first investors etc.
Prerequisites / NoticeThe course is practically oriented and features guest speakers from leading start-ups. The course embraces a unique perspective combining technology and investor thinking.
The seminar is structured around ten days.
Science in Perspective
Note that no more than six credits can be accredited in this category.
» Recommended Science in Perspective (Type B) for D-INFK
» see Science in Perspective: Language Courses ETH/UZH
» see Science in Perspective: Type A: Enhancement of Reflection Capability
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