Suchergebnis: Katalogdaten im Herbstsemester 2020

Informatik Master Information
Vertiefungsübergreifende Fächer
NummerTitelTypECTSUmfangDozierende
263-0006-00LAlgorithms Lab Information Belegung eingeschränkt - Details anzeigen
Only for master students, otherwise a special permission by the student administration of D-INFK is required.
O8 KP4P + 3AE. Welzl
KurzbeschreibungStudents learn how to solve algorithmic problems given by a textual description (understanding problem setting, finding appropriate modeling, choosing suitable algorithms, and implementing them). Knowledge of basic algorithms and data structures is assumed; more advanced material and usage of standard libraries for combinatorial algorithms are introduced in tutorials.
LernzielThe objective of this course is to learn how to solve algorithmic problems given by a textual description. This includes appropriate problem modeling, choice of suitable (combinatorial) algorithms, and implementing them (using C/C++, STL, CGAL, and BGL).
LiteraturT. Cormen, C. Leiserson, R. Rivest: Introduction to Algorithms, MIT Press, 1990.
J. Hromkovic, Teubner: Theoretische Informatik, Springer, 2004 (English: Theoretical Computer Science, Springer 2003).
J. Kleinberg, É. Tardos: Algorithm Design, Addison Wesley, 2006.
H. R. Lewis, C. H. Papadimitriou: Elements of the Theory of Computation, Prentice Hall, 1998.
T. Ottmann, P. Widmayer: Algorithmen und Datenstrukturen, Spektrum, 2012.
R. Sedgewick: Algorithms in C++: Graph Algorithms, Addison-Wesley, 2001.
263-0009-00LInformation Security Lab Information Belegung eingeschränkt - Details anzeigen
Only for master students, otherwise a special permission by the study administration of D-INFK is required.

Number of participants limited to 150.
O8 KP2V + 1U + 3P + 1AK. Paterson, D. Basin, S. Capkun, D. Hofheinz, A. Perrig
KurzbeschreibungThis InterFocus Course will provide a broad, hands-on introduction to Information Security, introducing adversarial thinking and security by design as key approaches to building secure systems.
LernzielThis course will introduce key concepts from Information Security, both from attack and defence perspectives. Students will gain an appreciation of the complexity and challenge of building secure systems.
InhaltThe course is organised in two-week segments. In each segment, a new concept from Information Security will be introduced. The overall scope will be broad, including cryptography, protocol design, network security, system security.
SkriptWill be made available during the semester.
LiteraturPaul C. van Oorschot, Computer Security and the Internet: Tools and Jewels.
Dan Boneh and Victor Shoup, A Graduate Course in Applied Cryptography.
Voraussetzungen / BesonderesIdeally, students will have taken the D-INFK Bachelors course “Information Security" or an equivalent course at Bachelors level.
Master-Studium (Studienreglement 2020)
Vertiefungen
Vertiefung in Data Management Systems
Kernfächer
NummerTitelTypECTSUmfangDozierende
263-3010-00LBig Data Information Belegung eingeschränkt - Details anzeigen W10 KP3V + 2U + 4AG. Fourny
KurzbeschreibungThe key challenge of the information society is to turn data into information, information into knowledge, knowledge into value. This has become increasingly complex. Data comes in larger volumes, diverse shapes, from different sources. Data is more heterogeneous and less structured than forty years ago. Nevertheless, it still needs to be processed fast, with support for complex operations.
LernzielThis combination of requirements, together with the technologies that have emerged in order to address them, is typically referred to as "Big Data." This revolution has led to a completely new way to do business, e.g., develop new products and business models, but also to do science -- which is sometimes referred to as data-driven science or the "fourth paradigm".

Unfortunately, the quantity of data produced and available -- now in the Zettabyte range (that's 21 zeros) per year -- keeps growing faster than our ability to process it. Hence, new architectures and approaches for processing it were and are still needed. Harnessing them must involve a deep understanding of data not only in the large, but also in the small.

The field of databases evolves at a fast pace. In order to be prepared, to the extent possible, to the (r)evolutions that will take place in the next few decades, the emphasis of the lecture will be on the paradigms and core design ideas, while today's technologies will serve as supporting illustrations thereof.

After visiting this lecture, you should have gained an overview and understanding of the Big Data landscape, which is the basis on which one can make informed decisions, i.e., pick and orchestrate the relevant technologies together for addressing each business use case efficiently and consistently.
InhaltThis course gives an overview of database technologies and of the most important database design principles that lay the foundations of the Big Data universe. We take the monolithic, one-machine relational stack from the 1970s, smash it down and rebuild it on top of large clusters: starting with distributed storage, and all the way up to syntax, models, validation, processing, indexing, and querying. A broad range of aspects is covered with a focus on how they fit all together in the big picture of the Big Data ecosystem.

No data is harmed during this course, however, please be psychologically prepared that our data may not always be in third normal form.

- physical storage: distributed file systems (HDFS), object storage(S3), key-value stores

- logical storage: document stores (MongoDB), column stores (HBase), graph databases (neo4j), data warehouses (ROLAP)

- data formats and syntaxes (XML, JSON, RDF, Turtle, CSV, XBRL, YAML, protocol buffers, Avro)

- data shapes and models (tables, trees, graphs, cubes)

- type systems and schemas: atomic types, structured types (arrays, maps), set-based type systems (?, *, +)

- an overview of functional, declarative programming languages across data shapes (SQL, XQuery, JSONiq, Cypher, MDX)

- the most important query paradigms (selection, projection, joining, grouping, ordering, windowing)

- paradigms for parallel processing, two-stage (MapReduce) and DAG-based (Spark)

- resource management (YARN)

- what a data center is made of and why it matters (racks, nodes, ...)

- underlying architectures (internal machinery of HDFS, HBase, Spark, neo4j)

- optimization techniques (functional and declarative paradigms, query plans, rewrites, indexing)

- applications.

Large scale analytics and machine learning are outside of the scope of this course.
LiteraturPapers from scientific conferences and journals. References will be given as part of the course material during the semester.
Voraussetzungen / BesonderesThis course, in the autumn semester, is only intended for:
- Computer Science students
- Data Science students
- CBB students with a Computer Science background

Mobility students in CS are also welcome and encouraged to attend. If you experience any issue while registering, please contact the study administration and you will be gladly added.

For students of all other departements interested in this fascinating topic: I would love to have you visit my lectures as well! So there is a series of two courses specially designed for you:
- "Information Systems for Engineers" (SQL, relational databases): this Fall
- "Big Data for Engineers" (similar to Big Data, but adapted for non Computer Scientists): Spring 2021
There is no hard dependency, so you can either them in any order, but it may be more enjoyable to start with Information Systems for Engineers.

Students who successfully completed Big Data for Engineers are not allowed to enrol in the course Big Data.
263-3845-00LData Management Systems Information W8 KP3V + 1U + 3AG. Alonso
KurzbeschreibungThe course will cover the implementation aspects of data management systems using relational database engines as a starting point to cover the basic concepts of efficient data processing and then expanding those concepts to modern implementations in data centers and the cloud.
LernzielThe goal of the course is to convey the fundamental aspects of efficient data management from a systems implementation perspective: storage, access, organization, indexing, consistency, concurrency, transactions, distribution, query compilation vs interpretation, data representations, etc. Using conventional relational engines as a starting point, the course will aim at providing an in depth coverage of the latest technologies used in data centers and the cloud to implement large scale data processing in various forms.
InhaltThe course will first cover fundamental concepts in data management: storage, locality, query optimization, declarative interfaces, concurrency control and recovery, buffer managers, management of the memory hierarchy, presenting them in a system independent manner. The course will place an special emphasis on understating these basic principles as they are key to understanding what problems existing systems try to address. It will then proceed to explore their implementation in modern relational engines supporting SQL to then expand the range of systems used in the cloud: key value stores, geo-replication, query as a service, serverless, large scale analytics engines, etc.
LiteraturThe main source of information for the course will be articles and research papers describing the architecture of the systems discussed. The list of papers will be provided at the beginning of the course.
Wahlfächer
NummerTitelTypECTSUmfangDozierende
252-0535-00LAdvanced Machine Learning Information W10 KP3V + 2U + 4AJ. M. Buhmann, C. Cotrini Jimenez
KurzbeschreibungMachine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects.
LernzielStudents will be familiarized with advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data.
InhaltThe theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data.

Topics covered in the lecture include:

Fundamentals:
What is data?
Bayesian Learning
Computational learning theory

Supervised learning:
Ensembles: Bagging and Boosting
Max Margin methods
Neural networks

Unsupservised learning:
Dimensionality reduction techniques
Clustering
Mixture Models
Non-parametric density estimation
Learning Dynamical Systems
SkriptNo lecture notes, but slides will be made available on the course webpage.
LiteraturC. Bishop. Pattern Recognition and Machine Learning. Springer 2007.

R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley &
Sons, second edition, 2001.

T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical
Learning: Data Mining, Inference and Prediction. Springer, 2001.

L. Wasserman. All of Statistics: A Concise Course in Statistical
Inference. Springer, 2004.
Voraussetzungen / BesonderesThe course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments.
Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution.

PhD students are required to obtain a passing grade in the course (4.0 or higher based on project and exam) to gain credit points.
252-1414-00LSystem Security Information W7 KP2V + 2U + 2AS. Capkun, A. Perrig
KurzbeschreibungThe first part of the lecture covers individual system aspects starting with tamperproof or tamper-resistant hardware in general over operating system related security mechanisms to application software systems, such as host based intrusion detection systems. In the second part, the focus is on system design and methodologies for building secure systems.
LernzielIn this lecture, students learn about the security requirements and capabilities that are expected from modern hardware, operating systems, and other software environments. An overview of available technologies, algorithms and standards is given, with which these requirements can be met.
InhaltThe first part of the lecture covers individual system's aspects starting with tamperproof or tamperresistant hardware in general over operating system related security mechanisms to application software systems such as host based intrusion detetction systems. The main topics covered are: tamper resistant hardware, CPU support for security, protection mechanisms in the kernel, file system security (permissions / ACLs / network filesystem issues), IPC Security, mechanisms in more modern OS, such as Capabilities and Zones, Libraries and Software tools for security assurance, etc.

In the second part, the focus is on system design and methodologies for building secure systems. Topics include: patch management, common software faults (buffer overflows, etc.), writing secure software (design, architecture, QA, testing), compiler-supported security, language-supported security, logging and auditing (BSM audit, dtrace, ...), cryptographic support, and trustworthy computing (TCG, SGX).

Along the lectures, model cases will be elaborated and evaluated in the exercises.
263-2800-00LDesign of Parallel and High-Performance Computing Information W9 KP3V + 2U + 3AT. Hoefler, M. Püschel
KurzbeschreibungAdvanced topics in parallel and high-performance computing.
LernzielUnderstand concurrency paradigms and models from a higher perspective and acquire skills for designing, structuring and developing possibly large parallel high-performance software systems. Become able to distinguish parallelism in problem space and in machine space. Become familiar with important technical concepts and with concurrency folklore.
InhaltWe will cover all aspects of high-performance computing ranging from architecture through programming up to algorithms. We will start with a discussion of caches and cache coherence in practical computer systems. We will dive into parallel programming concepts such as memory models, locks, and lock-free. We will cover performance modeling and parallel design principles as well as basic parallel algorithms.
Voraussetzungen / BesonderesThis class is intended for the Computer Science Masters curriculum. Students must have basic knowledge in programming in C as well as computer science theory. Students should be familiar with the material covered in the ETH computer science first-year courses "Parallele Programmierung (parallel programming)" and "Algorithmen und Datenstrukturen (algorithm and data structures)" or equivalent courses.
263-3210-00LDeep Learning Information Belegung eingeschränkt - Details anzeigen W8 KP3V + 2U + 2AT. Hofmann
KurzbeschreibungDeep learning is an area within machine learning that deals with algorithms and models that automatically induce multi-level data representations.
LernzielIn recent years, deep learning and deep networks have significantly improved the state-of-the-art in many application domains such as computer vision, speech recognition, and natural language processing. This class will cover the mathematical foundations of deep learning and provide insights into model design, training, and validation. The main objective is a profound understanding of why these methods work and how. There will also be a rich set of hands-on tasks and practical projects to familiarize students with this emerging technology.
Voraussetzungen / BesonderesThis is an advanced level course that requires some basic background in machine learning. More importantly, students are expected to have a very solid mathematical foundation, including linear algebra, multivariate calculus, and probability. The course will make heavy use of mathematics and is not (!) meant to be an extended tutorial of how to train deep networks with tools like Torch or Tensorflow, although that may be a side benefit.

The participation in the course is subject to the following condition:
- Students must have taken the exam in Advanced Machine Learning (252-0535-00) or have acquired equivalent knowledge, see exhaustive list below:

Advanced Machine Learning
https://ml2.inf.ethz.ch/courses/aml/

Computational Intelligence Lab
http://da.inf.ethz.ch/teaching/2019/CIL/

Introduction to Machine Learning
https://las.inf.ethz.ch/teaching/introml-S19

Statistical Learning Theory
http://ml2.inf.ethz.ch/courses/slt/

Computational Statistics
https://stat.ethz.ch/lectures/ss19/comp-stats.php

Probabilistic Artificial Intelligence
https://las.inf.ethz.ch/teaching/pai-f18
263-3850-00LInformal Methods Information W5 KP2G + 2AD. Cock
KurzbeschreibungFormal methods are increasingly a key part of the methodological toolkit of systems programmers - those writing operating systems, databases, and distributed systems. This course is about how to apply concepts, techniques, and principles from formal methods to such software systems, and how to get into the habit of thinking formally about systems design even when writing low-level C code.
LernzielThis course is about equipping students whose focus is systems with the insights and conceptual tools provided by formal methods, and thereby enabling them to become better systems programmers.
By the end of the course, students should be able to seamlessly integrate basic concepts form formal methods into how they conceive, design, implement, reason about, and debug computer systems.

The goal is not to provide a comprehensive introduction to formal methods - this is well covered by other courses in the department. Instead, it is intended to provide students in computer systems (who may or may not have existing background knowledge of formal methods) with a basis for applying formal methods in their work.
InhaltThis course does not assume prior knowledge of formal methods, and will start with a quick review of topics such static vs. dynamic reasoning, variants and invariants, program algebra and refinement, etc. However, it is strongly recommended that students have already taken one of the introductory formal methods course at ETH (or equivalents elsewhere) before taking this course - the emphasis is on reinforcing these concepts by applying them, not to teach them from scratch.

Instead, the majority of the course will be about how to apply these techniques to actual, practical code in real systems. We will work from real systems code written both by students taking the course, and practical systems developed using formal techniques, in particular the verified seL4 microkernel will be a key case study. We will also focus on informal, pen-and-paper arguments for correctness of programs and systems rather than using theorem provers or automated verification tools; again these latter techniques are well covered in other courses (and recommended as a complement to this one).
Vertiefung in Machine Intelligence
Kernfächer
NummerTitelTypECTSUmfangDozierende
252-0535-00LAdvanced Machine Learning Information W10 KP3V + 2U + 4AJ. M. Buhmann, C. Cotrini Jimenez
KurzbeschreibungMachine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects.
LernzielStudents will be familiarized with advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data.
InhaltThe theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data.

Topics covered in the lecture include:

Fundamentals:
What is data?
Bayesian Learning
Computational learning theory

Supervised learning:
Ensembles: Bagging and Boosting
Max Margin methods
Neural networks

Unsupservised learning:
Dimensionality reduction techniques
Clustering
Mixture Models
Non-parametric density estimation
Learning Dynamical Systems
SkriptNo lecture notes, but slides will be made available on the course webpage.
LiteraturC. Bishop. Pattern Recognition and Machine Learning. Springer 2007.

R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley &
Sons, second edition, 2001.

T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical
Learning: Data Mining, Inference and Prediction. Springer, 2001.

L. Wasserman. All of Statistics: A Concise Course in Statistical
Inference. Springer, 2004.
Voraussetzungen / BesonderesThe course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments.
Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution.

PhD students are required to obtain a passing grade in the course (4.0 or higher based on project and exam) to gain credit points.
263-3210-00LDeep Learning Information Belegung eingeschränkt - Details anzeigen W8 KP3V + 2U + 2AT. Hofmann
KurzbeschreibungDeep learning is an area within machine learning that deals with algorithms and models that automatically induce multi-level data representations.
LernzielIn recent years, deep learning and deep networks have significantly improved the state-of-the-art in many application domains such as computer vision, speech recognition, and natural language processing. This class will cover the mathematical foundations of deep learning and provide insights into model design, training, and validation. The main objective is a profound understanding of why these methods work and how. There will also be a rich set of hands-on tasks and practical projects to familiarize students with this emerging technology.
Voraussetzungen / BesonderesThis is an advanced level course that requires some basic background in machine learning. More importantly, students are expected to have a very solid mathematical foundation, including linear algebra, multivariate calculus, and probability. The course will make heavy use of mathematics and is not (!) meant to be an extended tutorial of how to train deep networks with tools like Torch or Tensorflow, although that may be a side benefit.

The participation in the course is subject to the following condition:
- Students must have taken the exam in Advanced Machine Learning (252-0535-00) or have acquired equivalent knowledge, see exhaustive list below:

Advanced Machine Learning
https://ml2.inf.ethz.ch/courses/aml/

Computational Intelligence Lab
http://da.inf.ethz.ch/teaching/2019/CIL/

Introduction to Machine Learning
https://las.inf.ethz.ch/teaching/introml-S19

Statistical Learning Theory
http://ml2.inf.ethz.ch/courses/slt/

Computational Statistics
https://stat.ethz.ch/lectures/ss19/comp-stats.php

Probabilistic Artificial Intelligence
https://las.inf.ethz.ch/teaching/pai-f18
263-5210-00LProbabilistic Artificial Intelligence Information Belegung eingeschränkt - Details anzeigen W8 KP3V + 2U + 2AA. Krause
KurzbeschreibungThis course introduces core modeling techniques and algorithms from machine learning, optimization and control for reasoning and decision making under uncertainty, and study applications in areas such as robotics and the Internet.
LernzielHow can we build systems that perform well in uncertain environments and unforeseen situations? How can we develop systems that exhibit "intelligent" behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? We will study core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet. The course is designed for graduate students.
InhaltTopics covered:
- Probability
- Probabilistic inference (variational inference, MCMC)
- Bayesian learning (Gaussian processes, Bayesian deep learning)
- Probabilistic planning (MDPs, POMPDPs)
- Multi-armed bandits and Bayesian optimization
- Reinforcement learning
Voraussetzungen / BesonderesSolid basic knowledge in statistics, algorithms and programming.
The material covered in the course "Introduction to Machine Learning" is considered as a prerequisite.
Wahlfächer
NummerTitelTypECTSUmfangDozierende
252-3005-00LNatural Language Processing Information Belegung eingeschränkt - Details anzeigen
Number of participants limited to 200.
W5 KP2V + 1U + 1AR. Cotterell
KurzbeschreibungThis course presents topics in natural language processing with an emphasis on modern techniques, primarily focusing on statistical and deep learning approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems.
LernzielThe objective of the course is to learn the basic concepts in the statistical processing of natural languages. The course will be project-oriented so that the students can also gain hands-on experience with state-of-the-art tools and techniques.
InhaltThis course presents an introduction to general topics and techniques used in natural language processing today, primarily focusing on statistical approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems.
LiteraturJacob Eisenstein: Introduction to Natural Language Processing (Adaptive Computation and Machine Learning series)
261-5100-00LComputational Biomedicine Information Belegung eingeschränkt - Details anzeigen
Number of participants limited to 60.
W5 KP2V + 1U + 1AG. Rätsch, V. Boeva, N. Davidson
KurzbeschreibungThe course critically reviews central problems in Biomedicine and discusses the technical foundations and solutions for these problems.
LernzielOver the past years, rapid technological advancements have transformed classical disciplines such as biology and medicine into fields of apllied data science. While the sheer amount of the collected data often makes computational approaches inevitable for analysis, it is the domain specific structure and close relation to research and clinic, that call for accurate, robust and efficient algorithms. In this course we will critically review central problems in Biomedicine and will discuss the technical foundations and solutions for these problems.
InhaltThe course will consist of three topic clusters that will cover different aspects of data science problems in Biomedicine:
1) String algorithms for the efficient representation, search, comparison, composition and compression of large sets of strings, mostly originating from DNA or RNA Sequencing. This includes genome assembly, efficient index data structures for strings and graphs, alignment techniques as well as quantitative approaches.
2) Statistical models and algorithms for the assessment and functional analysis of individual genomic variations. this includes the identification of variants, prediction of functional effects, imputation and integration problems as well as the association with clinical phenotypes.
3) Models for organization and representation of large scale biomedical data. This includes ontolgy concepts, biomedical databases, sequence annotation and data compression.
Voraussetzungen / BesonderesData Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line
263-2400-00LReliable and Interpretable Artificial Intelligence Information W6 KP2V + 2U + 1AM. Vechev
KurzbeschreibungCreating reliable and explainable probabilistic models is a fundamental challenge to solving the artificial intelligence problem. This course covers some of the latest and most exciting advances that bring us closer to constructing such models.
LernzielThe main objective of this course is to expose students to the latest and most exciting research in the area of explainable and interpretable artificial intelligence, a topic of fundamental and increasing importance. Upon completion of the course, the students should have mastered the underlying methods and be able to apply them to a variety of problems.

To facilitate deeper understanding, an important part of the course will be a group hands-on programming project where students will build a system based on the learned material.
InhaltThe course covers some of the latest research (over the last 2-3 years) underlying the creation of safe, trustworthy, and reliable AI (more information here: https://www.sri.inf.ethz.ch/teaching/riai2020):

* Adversarial Attacks on Deep Learning (noise-based, geometry attacks, sound attacks, physical attacks, autonomous driving, out-of-distribution)
* Defenses against attacks
* Combining gradient-based optimization with logic for encoding background knowledge
* Complete Certification of deep neural networks via automated reasoning (e.g., via numerical abstractions, mixed-integer solvers).
* Probabilistic certification of deep neural networks
* Training deep neural networks to be provably robust via automated reasoning
* Understanding and Interpreting Deep Networks
* Probabilistic Programming
Voraussetzungen / BesonderesWhile not a formal requirement, the course assumes familiarity with basics of machine learning (especially probability theory, linear algebra, gradient descent, and neural networks). These topics are usually covered in “Intro to ML” classes at most institutions (e.g., “Introduction to Machine Learning” at ETH).

For solving assignments, some programming experience in Python is excepted.
263-4500-00LAdvanced Algorithms Information W9 KP3V + 2U + 3AM. Ghaffari
KurzbeschreibungThis is a graduate-level course on algorithm design (and analysis). It covers a range of topics and techniques in approximation algorithms, sketching and streaming algorithms, and online algorithms.
LernzielThis course familiarizes the students with some of the main tools and techniques in modern subareas of algorithm design.
InhaltThe lectures will cover a range of topics, tentatively including the following: graph sparsifications while preserving cuts or distances, various approximation algorithms techniques and concepts, metric embeddings and probabilistic tree embeddings, online algorithms, multiplicative weight updates, streaming algorithms, sketching algorithms, and derandomization.
Skripthttps://people.inf.ethz.ch/gmohsen/AA20/
Voraussetzungen / BesonderesThis course is designed for masters and doctoral students and it especially targets those interested in theoretical computer science, but it should also be accessible to last-year bachelor students.

Sufficient comfort with both (A) Algorithm Design & Analysis and (B) Probability & Concentrations. E.g., having passed the course Algorithms, Probability, and Computing (APC) is highly recommended, though not required formally. If you are not sure whether you're ready for this class or not, please consult the instructor.
263-5902-00LComputer Vision Information W8 KP3V + 1U + 3AM. Pollefeys, S. Tang, V. Ferrari
KurzbeschreibungThe goal of this course is to provide students with a good understanding of computer vision and image analysis techniques. The main concepts and techniques will be studied in depth and practical algorithms and approaches will be discussed and explored through the exercises.
LernzielThe objectives of this course are:
1. To introduce the fundamental problems of computer vision.
2. To introduce the main concepts and techniques used to solve those.
3. To enable participants to implement solutions for reasonably complex problems.
4. To enable participants to make sense of the computer vision literature.
InhaltCamera models and calibration, invariant features, Multiple-view geometry, Model fitting, Stereo Matching, Segmentation, 2D Shape matching, Shape from Silhouettes, Optical flow, Structure from motion, Tracking, Object recognition, Object category recognition
Voraussetzungen / BesonderesIt is recommended that students have taken the Visual Computing lecture or a similar course introducing basic image processing concepts before taking this course.
Vertiefung in Secure and Reliable Systems
Kernfächer
NummerTitelTypECTSUmfangDozierende
252-0237-00LConcepts of Object-Oriented Programming Information W8 KP3V + 2U + 2AP. Müller
KurzbeschreibungCourse that focuses on an in-depth understanding of object-oriented programming and compares designs of object-oriented programming languages. Topics include different flavors of type systems, inheritance models, encapsulation in the presence of aliasing, object and class initialization, program correctness, reflection
LernzielAfter this course, students will:
Have a deep understanding of advanced concepts of object-oriented programming and their support through various language features. Be able to understand language concepts on a semantic level and be able to compare and evaluate language designs.
Be able to learn new languages more rapidly.
Be aware of many subtle problems of object-oriented programming and know how to avoid them.
InhaltThe main goal of this course is to convey a deep understanding of the key concepts of sequential object-oriented programming and their support in different programming languages. This is achieved by studying how important challenges are addressed through language features and programming idioms. In particular, the course discusses alternative language designs by contrasting solutions in languages such as C++, C#, Eiffel, Java, Python, and Scala. The course also introduces novel ideas from research languages that may influence the design of future mainstream languages.

The topics discussed in the course include among others:
The pros and cons of different flavors of type systems (for instance, static vs. dynamic typing, nominal vs. structural, syntactic vs. behavioral typing)
The key problems of single and multiple inheritance and how different languages address them
Generic type systems, in particular, Java generics, C# generics, and C++ templates
The situations in which object-oriented programming does not provide encapsulation, and how to avoid them
The pitfalls of object initialization, exemplified by a research type system that prevents null pointer dereferencing
How to maintain the consistency of data structures
LiteraturWill be announced in the lecture.
Voraussetzungen / BesonderesPrerequisites:
Mastering at least one object-oriented programming language (this course will NOT provide an introduction to object-oriented programming); programming experience
252-0463-00LSecurity Engineering Information W7 KP2V + 2U + 2AD. Basin, S. Krstic
KurzbeschreibungSubject of the class are engineering techniques for developing secure systems. We examine concepts, methods and tools, applied within the different activities of the SW development process to improve security of the system. Topics: security requirements&risk analysis, system modeling&model-based development methods, implementation-level security, and evaluation criteria for secure systems
LernzielSecurity engineering is an evolving discipline that unifies two important areas: software engineering and security. Software Engineering addresses the development and application of methods for systematically developing, operating, and maintaining, complex, high-quality software.
Security, on the other hand, is concerned with assuring and verifying properties of a system that relate to confidentiality, integrity, and availability of data.

The goal of this class is to survey engineering techniques for developing secure systems. We will examine concepts, methods, and tools that can be applied within the different activities of the software development process, in order to improve the security of the resulting systems.

Topics covered include

* security requirements & risk analysis,
* system modeling and model-based development methods,
* implementation-level security, and
* evaluation criteria for the development of secure systems
InhaltSecurity engineering is an evolving discipline that unifies two important areas: software engineering and security. Software Engineering addresses the development and application of methods for systematically developing, operating, and maintaining, complex, high-quality software.
Security, on the other hand, is concerned with assuring and verifying properties of a system that relate to confidentiality, integrity, and availability of data.

The goal of this class is to survey engineering techniques for developing secure systems. We will examine concepts, methods, and tools that can be applied within the different activities of the software development process, in order to improve the security of the resulting systems.

Topics covered include

* security requirements & risk analysis,
* system modeling and model-based development methods,
* implementation-level security, and
* evaluation criteria for the development of secure systems

Modules taught:

1. Introduction
- Introduction of Infsec group and speakers
- Security meets SW engineering: an introduction
- The activities of SW engineering, and where security fits in
- Overview of this class
2. Requirements Engineering: Security Requirements and some Analysis
- overview: functional and non-functional requirements
- use cases, misuse cases, sequence diagrams
- safety and security
- FMEA, FTA, attack trees
3. Modeling in the design activities
- structure, behavior, and data flow
- class diagrams, statecharts
4. Model-driven security for access control (design)
- SecureUML as a language for access control
- Combining Design Modeling Languages with SecureUML
- Semantics, i.e., what does it all mean,
- Generation
- Examples and experience
5. Model-driven security (Part II)
- Continuation of above topics
6. Security patterns (design and implementation)
7. Implementation-level security
- Buffer overflows
- Input checking
- Injection attacks
8. Testing
- overview
- model-based testing
- testing security properties
9. Risk analysis and management 1 (project management)
- "risk": assets, threats, vulnerabilities, risk
- risk assessment: quantitative and qualitative
- safeguards
- generic risk analysis procedure
- The OCTAVE approach
10. Risk analysis: IT baseline protection
- Overview
- Example
11. Evaluation criteria
- CMMI
- systems security engineering CMM
- common criteria
12. Guest lecture
- TBA
Literatur- Ross Anderson: Security Engineering, Wiley, 2001.
- Matt Bishop: Computer Security, Pearson Education, 2003.
- Ian Sommerville: Software Engineering, 6th ed., Addison-Wesley, 2001.
- John Viega, Gary McGraw: Building Secure Software, Addison-Wesley, 2002.
- Further relevant books and journal/conference articles will be announced in the lecture.
Voraussetzungen / BesonderesPrerequisite: Class on Information Security
252-1414-00LSystem Security Information W7 KP2V + 2U + 2AS. Capkun, A. Perrig
KurzbeschreibungThe first part of the lecture covers individual system aspects starting with tamperproof or tamper-resistant hardware in general over operating system related security mechanisms to application software systems, such as host based intrusion detection systems. In the second part, the focus is on system design and methodologies for building secure systems.
LernzielIn this lecture, students learn about the security requirements and capabilities that are expected from modern hardware, operating systems, and other software environments. An overview of available technologies, algorithms and standards is given, with which these requirements can be met.
InhaltThe first part of the lecture covers individual system's aspects starting with tamperproof or tamperresistant hardware in general over operating system related security mechanisms to application software systems such as host based intrusion detetction systems. The main topics covered are: tamper resistant hardware, CPU support for security, protection mechanisms in the kernel, file system security (permissions / ACLs / network filesystem issues), IPC Security, mechanisms in more modern OS, such as Capabilities and Zones, Libraries and Software tools for security assurance, etc.

In the second part, the focus is on system design and methodologies for building secure systems. Topics include: patch management, common software faults (buffer overflows, etc.), writing secure software (design, architecture, QA, testing), compiler-supported security, language-supported security, logging and auditing (BSM audit, dtrace, ...), cryptographic support, and trustworthy computing (TCG, SGX).

Along the lectures, model cases will be elaborated and evaluated in the exercises.
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