Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

Search result: Catalogue data in Spring Semester 2019

Computer Science Teaching Diploma Information
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Educational Science
Course offerings in the category Educational Science are listed under "Programme: Educational Science for Teaching Diploma and TC".
NumberTitleTypeECTSHoursLecturers
851-0240-01LDesigning Learning Environments for School (EW2 TD) Information Restricted registration - show details
Prerequisites: successful participation in 851-0240-00L "Human Learning (EW1)".

Adresses to students enrolled either in Teaching Diploma* (TD) or Teaching Certificate (TC) in Computer Science, Mathematics or Physics.
*Except for students of Sport Teaching Diploma, who complete the sport-specific course unit EW2.
O3 credits2VE. Stern, P. Greutmann, J. Maue
AbstractTeaching is a complex skill. The lecture comprises (a) presentations about the theoretical background of this skill, (b) discussions of practical aspects, and (c) practical exercises.
ObjectiveThe participants have the conceptual und procedural knowledge, and skills necessary for long-term planning, preparing, and implementing good lessons. They can apply this knowledge on different topics of their scientific STEM-background.
ContentWe discuss characteristics of successful lessons and how to design such lessons by using curricula and lesson plans, teaching goals and a variety of teaching methods.
Lecture notesThe lecture comprises interactive parts where the participants elaborate and extend their knowledge and skills. Thus, there is no comprehensive written documentation of the lecture. The participants can download presentation slides, learning materials, and templates from "Moodle".
LiteratureThe necessary literature can be downloaded from "Moodle".
Prerequisites / NoticeThe lecture EW2 can only be attended by students who already successfully completed the lecture Human Learning (EW1).
There will be two independent lectures for different groups of students. You will get further information in an email at the beginning of the semester.
To get the Credits you have to
- regularly attend to the lecture
- have the grade 4 or higher in the final written exam.
851-0240-24LDesigning Learning Environments for Schools (EW2 LD) - Portfolio
- Enrolment only possible with simultaneous enrolment in course 851-0240-01L Designing Learning Environments for School (EW2 LD)!

- Prerequisites: successful participation in 851-0240-00L "Human Learning (EW1)".

- Adresses to students enrolled either in Teaching Diploma* (TD) or Teaching Certificate (TC) in Computer Science, Mathematics or Physics.
*Except for students of Sport Teaching Diploma, who complete the sport-specific course unit EW2.
O1 credit2UP. Greutmann, J. Maue
AbstractIn this lecture, you design a portfolio, i.e. a complete and elaborated teaching enviroment for schools, based on your scientific STEM-background
ObjectiveThis lecture is an implementation and transfer of the theoretical inputs provided by the lecture "Designing Learning Environments for School" (EW2).
851-0242-11LGender Issues In Education and STEM Restricted registration - show details
Number of participants limited to 20.

Enrolment only possible with matriculation in Teaching Diploma or Teaching Certificate (excluding Teaching Diploma Sport).

Prerequisite: students should be taking the course 851-0240-00L Human Learning (EW1) in parallel, or to have successfully completed it.
W2 credits2SM. Berkowitz Biran, C. M. Thurn
AbstractIn this seminar, we will introduce some of the major gender-related issues in the context of education and science learning, such as the under-representation of girls and women in science, technology, engineering and mathematics (STEM). Common perspectives, controversies and empirical evidence will be discussed.
Objective- To familiarize students with gender issues in the educational and STEM context and with controversies regarding these issues
- To develop a critical view on existing perspectives.
- To integrate this knowledge with teacher's work.
ContentWhy do fewer women than men specialize in STEM (science, technology, engineering and mathematics)? Are girls better in language and boys better in math? These and other questions about gender differences relevant to education and STEM learning have been occupying researchers for decades. In this seminar, students will learn about major gender issues in the educational context and the different perspectives for understanding them.

Students will read and critically discuss selected publications on these topics and their implications for the classroom context. There will be weekly (or bi-weekly) assignments as well as a final project in which students will integrate and elaborate on the topics learned in the seminar.
Prerequisites / NoticeRecommended: Completion of the course 851-0240-00L Human Learning (EW1).

Active participation in the seminar.
851-0242-08LResearch Methods in Educational Science Restricted registration - show details
Number of participants limited to 30.

This course unit can only be enroled after successful participation in, or imultaneous enrolment in the course 851-0240-00L "Human Learning (EW 1)" .
W1 credit1SP. Edelsbrunner, T. Braas, Z. Lue, C. M. Thurn
AbstractLiterature from learning sciences will be read and discussed. Research methods will be in focus.
At the first meeting all participants will be allocated to working groups and two further meetings will be set up with the groups.
In the small groups students will write critical short essays about the read literature. The essays will be presented and discussed in the plenum at the third meeting.
Objective- Understand research methods used in the empirical educational sciences
- Understand and critically examine information from scientific journals and media
- Understand pedagogically relevant findings from the empirical educational sciences
» see Educational Science Teaching Diploma
Subject Didactics in Computer Science
Important: You can only enrole in the courses of this category if you have not more than 12 CP left for possible additional requirements.
NumberTitleTypeECTSHoursLecturers
272-0102-00LSubject Didactics of Computer Science II Information Restricted registration - show details
Prerequisite: Subject Didactics of Computer Science I
O4 credits3GJ. Hromkovic, G. Serafini
AbstractThis course deals primarily with didactic aspects of computer science and its key contributions to general education nurturing the youths' development of a unique and indispensable way of thinking while at the same time leading to their understanding of our world and to higher education entrance qualifications.
ObjectiveThis course deals primarily with didactic aspects of computer science and its key contributions to general education nurturing the youths' development of a unique and indispensable way of thinking while at the same time leading to their understanding of our world and to higher education entrance qualifications.

Subject Didactics in Computer Science II deals with the adequate choice of educational content for computer science, taking in account its comprehensibility for different age groups and the didactic methods suitable for a succesful knowledge transfer.

Within the scope of a semester exercise the students develop and document an adaptive teaching unit for computer science. They improve their practical knowledge of the teaching methods and techniques introduced in Subject Didactics in Computer Science I.

The aim of the course is to combine the mathematical and algorithmic way of thinking with the approaches adopted by engineering disciplines.

The students understand the basic concepts of computer science in the context of a broad and deep knowledge. Through this understanding, they manage to prepare teaching materials for a successful knowledge transfer and to pass their passion for the subject on to their pupils.

The students know various teaching methods including their advantages and disadvantages. They can handle unequal levels of the learners' prior knowledge. Besides of holding classes the students do care about the individual pupil support. They encourage the autonomy of the learners. They manage to work with diverse target groups and to establish a positive climate for learning.

The students are able to express themselves using a comprehensible and refined professional language, both spoken and written, and they master the basic terms of computer science. Besides of the English terminology they are familiar with the german expressions. The students are able to produce detailed, matured, linguistically correct and design-wise appealing teaching materials.
ContentThe main themes of Subject Didactics of Computer Science II are Cryptology and Computability. The unit focuses on the content of computer science that conveys general educational values. this aims at the understanding of basic concepts of the science, such as
- Algorithm
- Complexity
- Determinism
- Nondeterminism
- Probability
- Computation
Lecture notesUnterlagen und Folien werden zur Verfügung gestellt.
LiteratureJ. Hromkovic: Sieben Wunder der Informatik: Eine Reise an die Grenze des Machbaren, mit Aufgaben und Lösungen. Vieweg+Teubner; Auflage: 2 (2008).

K. Freiermuth, J. Hromkovic, L. Keller und B. Steffen: Einfuehrung in die Kryptologie: Lehrbuch für Unterricht und Selbststudium. Springer Vieweg; Auflage: 2 (2014).

J. Hromkovic: Berechenbarkeit: Logik, Argumentation, Rechner und Assembler, Unendlichkeit, Grenzen der Automatisierbarkeit. Vieweg+Teubner; Auflage: 1 (2011).

H.-J. Böckenhauer, J. Hromkovic: Formale Sprachen: Endliche Automaten, Grammatiken, lexikalische und syntaktische Analyse. Springer Vieweg; Auflage: 1 (Januar 2013).
Prerequisites / NoticeBewilligung der Dozierenden für alle Studierenden notwendig
272-0103-00LMentored Work Subject Didactics Computer Science A Information Restricted registration - show details
Mentored Work Subject Didactics in Computer Science for TC, Teaching Diploma.
O2 credits4AJ. Hromkovic, G. Serafini
AbstractIn their mentored work on subject didactics, students put into practice the contents of the subject-didactics lectures and go into these in greater depth. Under supervision, they compile tuition materials that are conducive to learning and/or analyse and reflect on certain topics from a subject-based and pedagogical angle.
ObjectiveThe objective is for the students:
- to be able to familiarise themselves with a tuition topic by consulting different sources, acquiring materials and reflecting on the relevance of the topic and the access they have selected to this topic from a specialist, subject-didactics and pedagogical angle and potentially from a social angle too.
- to show that they can independently compile a tuition sequence that is conducive to learning and develop this to the point where it is ready for use.
ContentThematische Schwerpunkte
Die Gegenstände der mentorierten Arbeit in Fachdidaktik stammen in der Regel aus dem gymnasialen Unterricht.

Lernformen
Alle Studierenden erhalten ein individuelles Thema und erstellen dazu eine eigenständige Arbeit. Sie werden dabei von ihrer Betreuungsperson begleitet. Gegebenenfalls stellen sie ihre Arbeit oder Aspekte daraus in einem Kurzvortrag vor. Die mentorierte Arbeit ist Teil des Portfolios der Studierenden.
Lecture notesEine kurze Anleitung zur mentorierten Arbeit in Fachdidaktik wird zur Verfügung gestellt.
LiteratureDie Literatur ist themenspezifisch. Die Studierenden beschaffen sie sich in der Regel selber (siehe Lernziele). In besonderen Fällen wird sie vom Betreuer zur Verfügung gestellt.
Prerequisites / NoticeDie Arbeit sollte vor Beginn des Praktikums abgeschlossen werden.
272-0104-00LMentored Work Subject Didactics Computer Science B Restricted registration - show details
Mentored Work Subject Didactics in Computer Science for Teaching Diploma and for students upgrading TC to Teaching Diploma.
O2 credits4AJ. Hromkovic, G. Serafini
AbstractIn their mentored work on subject didactics, students put into practice the contents of the subject-didactics lectures and go into these in greater depth. Under supervision, they compile tuition materials that are conducive to learning and/or analyse and reflect on certain topics from a subject-based and pedagogical angle.
ObjectiveThe objective is for the students:
- to be able to familiarise themselves with a tuition topic by consulting different sources, acquiring materials and reflecting on the relevance of the topic and the access they have selected to this topic from a specialist, subject-didactics and pedagogical angle and potentially from a social angle too.
- to show that they can independently compile a tuition sequence that is conducive to learning and develop this to the point where it is ready for use.
ContentThematische Schwerpunkte
Die Gegenstände der mentorierten Arbeit in Fachdidaktik stammen in der Regel aus dem gymnasialen Unterricht.

Lernformen
Alle Studierenden erhalten ein individuelles Thema und erstellen dazu eine eigenständige Arbeit. Sie werden dabei von ihrer Betreuungsperson begleitet. Gegebenenfalls stellen sie ihre Arbeit oder Aspekte daraus in einem Kurzvortrag vor. Die mentorierte Arbeit ist Teil des Portfolios der Studierenden.
Lecture notesEine kurze Anleitung zur mentorierten Arbeit in Fachdidaktik wird zur Verfügung gestellt.
LiteratureDie Literatur ist themenspezifisch. Die Studierenden beschaffen sie sich in der Regel selber (siehe Lernziele). In besonderen Fällen wird sie vom Betreuer zur Verfügung gestellt.
Prerequisites / NoticeDie Arbeit sollte vor Beginn des Praktikums abgeschlossen werden.
Professional Training
Important: You can only enrole in the courses of this category if you have not more than 12 CP left for possible additional requirements.
NumberTitleTypeECTSHoursLecturers
272-0202-00LProfessional Exercises Information Restricted registration - show details O2 credits4UG. Serafini, J. Hromkovic
AbstractIn the course Professional Exercises the students achieve additional school-relevant experiences. The students carry out individually specified, practice related projects, in which they support, document or reflect on learning processes.
ObjectiveAchievement of additional school-relevant experiences. The students carry out individually specified, practice related projects, in which they support, document or reflect on learning processes.
ContentThe course Professional Exercises offers the opportunity for additional school-relevant activities related to practice.
The students are supported by the lecturers or by experienced teachers. They assist teachers at school, they create training systems and tests, correct the written homework of pupils and evaluate the progress of a class. The students create explanations and detailed solutions to exercises with respect to the actual knowledge of the pupils. A written assignment states the exact scope of the activity.
272-0203-00LTeaching Internship in Computer Science Restricted registration - show details O8 credits17PJ. Hromkovic, G. Serafini
AbstractThe teaching practice takes in 50 lessons: 30 are taught by the students, and the students sit in on 20 lessons. The teaching practice lasts 4-6 weeks. It gives students the opportunity to implement the contents of their specialist-subject, educational science and subject-didactics training in the classroom. Students also conduct work assignments in parallel to their teaching practice.
Objective- Students use their specialist-subject, educational-science and subject-didactics training to draw up concepts for teaching.
- They are able to assess the significance of tuition topics in their subject from different angles (including interdisciplinary angles) and impart these to their pupils.
- They acquire the skills of the teaching trade.
- They practise finding the balance between instruction and openness so that pupils can and, indeed, must make their own cognitive contribution.
- They learn to assess pupils' work.
- Together with the teacher in charge of their teacher training, the students constantly evaluate their own performance.
ContentDie Studierenden sammeln Erfahrungen in der Unterrichtsführung, der Auseinandersetzung mit Lernenden, der Klassenbetreuung und der Leistungsbeurteilung. Zu Beginn des Praktikums plant die Praktikumslehrperson gemeinsam mit dem/der Studierenden das Praktikum und die Arbeitsaufträge. Die schriftlich dokumentierten Ergebnisse der Arbeitsaufträge sind Bestandteil des Portfolios der Studierenden. Anlässlich der Hospitationen erläutert die Praktikumslehrperson ihre fachlichen, fachdidaktischen und pädagogischen Überlegungen, auf deren Basis sie den Unterricht geplant hat und tauscht sich mit dem/der Studierenden aus. Die von dem/der Studierenden gehaltenen Lektionen werden vor- und nachbesprochen. Die Praktikumslehrperson sorgt ausserdem dafür, dass der/die Studierende Einblick in den schulischen Alltag erhält und die vielfältigen Verpflichtungen einer Lehrperson kennen lernt.
LiteratureWird von der Praktikumslehrperson bestimmt.
Prerequisites / NoticeFindet in der Regel am Schluss der Ausbildung, vor Ablegung der Prüfungslektionen und vor der Lerneinheit „Lernwirksam unterrichten“ statt.
272-0204-00LTeaching Internship in Computer Science II Restricted registration - show details
Teaching Internship for students upgrading TC to Teaching Diploma.
W4 credits9PJ. Hromkovic, G. Serafini
AbstractThis is a supplement to the Teaching Internship required to obtain a Teaching Diploma in the corresponding subject. It is aimed at enlarging the already acquired teaching experience. Students observe 10 lessons and teach 15 lessons independently.
ObjectiveDie Studierenden können die Bedeutung von Unterrichtsthemen in ihrem Fach unter verschiedenen Blickwinkeln einschätzen. Sie kennen und beherrschen das unterrichtliche Handwerk. Sie können ein gegebenes Unterrichtsthema für eine Gruppe von Lernenden fachlich und didaktisch korrekt strukturieren und in eine adäquate Lernumgebung umsetzen. Es gelingt ihnen, die Balance zwischen Anleitung und Offenheit zu finden, sodass die Lernenden sowohl über den nötigen Freiraum wie über ausreichend Orientierung verfügen, um aktiv und effektiv flexibel nutzbares (Fach-)Wissen zu erwerben.
ContentDas Aufbaupraktikum richtet sich an Studierende, die bereits das Didaktik-Zertifikat in ihrem Fach erworben haben und nun eine Aufbauausbildung zum Lehrdiplom für Maturitätsschulen absolvieren. In diesem zusätzlichen Praktikum sollen die Studierenden vertiefte unterrichtliche Erfahrungen machen. Auf der Grundlage der zusätzlich erworbenen Kenntnisse und mit Hilfe der ihnen jetzt zu Verfügung stehenden Instrumente analysieren sie verschiedene Aspekte des hospitierten Unterrichts. In dem von ihnen selbst gestalteten Unterricht nutzen sie beim Entwurf, bei der Durchführung und der Beurteilung ihrer Arbeit insbesondere die zusätzlich gewonnen Erkenntnisse aus der allgemeinen und fachdidaktischen Lehr- und Lernforschung.
LiteratureWird von der Praktikumslehrperson bestimmt.
Prerequisites / NoticeFindet in der Regel am Schluss der Ausbildung, vor Ablegung der Prüfungslektionen und vor der Lerneinheit „Lernwirksam unterrichten“ statt.
272-0205-01LExamination Lesson I in Computer Science Information Restricted registration - show details
Simultaneous enrolment in "Examination Lesson II in Computer Science" (272-0205-02L) is compulsory.
O1 credit2PJ. Hromkovic, G. Serafini
AbstractIn the context of an examination lesson conducted and graded at a high school, the candidates provide evidence of the subject-matter-based and didactic skills they have acquired in the course of their training.
ObjectiveOn the basis of a specified topic, the candidate shows that they are in a position
- to develop and conduct teaching that is conducive to learning at high school level, substantiating it in terms of the subject-matter and from the didactic angle
- to analyze the tuition they have given with regard to its strengths and weaknesses, and outline improvements.
ContentDie Studierenden erfahren das Lektionsthema in der Regel eine Woche vor dem Prüfungstermin. Von der zuständigen Lehrperson erhalten sie Informationen über den Wissensstand der zu unterrichtenden Klasse und können sie vor dem Prüfungstermin besuchen.
Sie erstellen eine Vorbereitung gemäss Anleitung und reichen sie bis am Vortrag um 12 Uhr den beiden Prüfungsexperten ein.
Die gehaltene Lektion wird kriteriumsbasiert beurteilt. Die Beurteilung umfasst auch die schriftliche Vorbereitung und eine mündliche Reflexion des Kandidaten/der Kandidatin über die gehaltene Lektion im Rahmen eines kurzen Kolloquiums.
Lecture notesDokument: Schriftliche Vorbereitung für Prüfungslektionen.
Prerequisites / NoticeNach Abschluss der übrigen Ausbildung, vor der Lerneinheit „Lernwirksam unterrichten“.
272-0205-02LExamination Lesson II in Computer Science Information Restricted registration - show details
Simultaneous enrolment in "Examination Lesson I in Computer Science" (272-0205-01L) is compulsory.
O1 credit2PJ. Hromkovic, G. Serafini
AbstractIn the context of an examination lesson conducted and graded at a high school, the candidates provide evidence of the subject-matter-based and didactic skills they have acquired in the course of their training.
ObjectiveOn the basis of a specified topic, the candidate shows that they are in a position
- to develop and conduct teaching that is conducive to learning at high school level, substantiating it in terms of the subject-matter and from the didactic angle
- to analyze the tuition they have given with regard to its strengths and weaknesses, and outline improvements.
ContentDie Studierenden erfahren das Lektionsthema in der Regel eine Woche vor dem Prüfungstermin. Von der zuständigen Lehrperson erhalten sie Informationen über den Wissensstand der zu unterrichtenden Klasse und können sie vor dem Prüfungstermin besuchen.
Sie erstellen eine Vorbereitung gemäss Anleitung und reichen sie bis am Vortag um 12 Uhr den beiden Prüfungsexperten ein.
Die gehaltene Lektion wird kriteriumsbasiert beurteilt. Die Beurteilung umfasst auch die schriftliche Vorbereitung und eine mündliche Reflexion des Kandidaten/ der Kandidatin über die gehaltene Lektion im Rahmen eines kurzen Kolloquiums.
Lecture notesDokument: Schriftliche Vorbereitung für Prüfungslektionen.
Prerequisites / NoticeNach Abschluss der übrigen Ausbildung, vor der Lerneinheit „Lernwirksam unterrichten“.
Spec. Courses in Resp. Subj. w/ Educ. Focus & Further Subj. Didactics
NumberTitleTypeECTSHoursLecturers
272-0301-00LMethods for Design of Random Systems Information
Does not take place this semester.
This course d o e s n o t include the Mentored Work Specialised Courses with an Educational Focus in Computer Science B.
W4 credits2V + 1U
AbstractThe students should get a deep understanding of the notion of randomness and its usefulness. Using basic elements probability theory and number theory the students will discover randomness as a source of efficiency in algorithmic. The goal is to teach the paradigms of design of randomized algorithms.
ObjectiveTo understand the computational power of randomness and to learn the basic
methods for designing randomized algorithms
Lecture notesJ. Hromkovic: Randomisierte Algorithmen, Teubner 2004.

J.Hromkovic: Design and Analysis of Randomized Algorithms. Springer 2006.

J.Hromkovic: Algorithmics for Hard Problems, Springer 2004.
LiteratureJ. Hromkovic: Randomisierte Algorithmen, Teubner 2004.

J.Hromkovic: Design and Analysis of Randomized Algorithms. Springer 2006.

J.Hromkovic: Algorithmics for Hard Problems, Springer 2004.
272-0302-00LApproximation and Online Algorithms Information W4 credits2V + 1UH.‑J. Böckenhauer, D. 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.
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
272-0400-00LMentored Work Specialised Courses in the Respective Subject with Educational Focus Computer Sc A Information Restricted registration - show details O2 credits4AJ. Hromkovic, G. Serafini
AbstractIn the mentored work on their subject specialisation, students link high-school and university aspects of the subject, thus strengthening their teaching competence with regard to curriculum decisions and the future development of the tuition. They compile texts under supervision that are directly comprehensible to the targeted readers - generally specialist-subject teachers at high-school level.
ObjectiveThe aim is for the students
- to familiarise themselves with a new topic by obtaining material and studying the sources, so that they can selectively extend their specialist competence in this way.
- to independently develop a text on the topic, with special focus on its mathematical comprehensibility in respect of the level of knowledge of the targeted readership.
- To try out different options for specialist further training in their profession.
ContentThematische Schwerpunkte:
Die mentorierte Arbeit in FV besteht in der Regel in einer Literaturarbeit über ein Thema, das einen Bezug zum gymnasialem Unterricht oder seiner Weiterentwicklung hat. Die Studierenden setzen darin Erkenntnisse aus den Vorlesungen in FV praktisch um.

Lernformen:
Alle Studierenden erhalten ein individuelles Thema und erstellen dazu eine eigenständige Arbeit. Sie werden dabei von ihrer Betreuungsperson begleitet. Gegebenenfalls stellen sie ihre Arbeit oder Aspekte daraus in einem Kurzvortrag vor. Die mentorierte
Arbeit ist Teil des Portfolios der Studierenden.
Lecture notesEine Anleitung zur mentorierten Arbeit in FV wird zur Verfügung gestellt.
LiteratureDie Literatur ist themenspezifisch. Sie muss je nach Situation selber beschafft werden oder wird zur Verfügung gestellt.
Prerequisites / NoticeDie Arbeit sollte vor Beginn des Praktikums abgeschlossen werden.
272-0401-00LMentored Work Specialised Courses in the Respective Subject with Educational Focus Computer Sc B Information Restricted registration - show details O2 credits4AJ. Hromkovic, G. Serafini
AbstractIn the mentored work on their subject specialisation, students link high-school and university aspects of the subject, thus strengthening their teaching competence with regard to curriculum decisions and the future development of the tuition. They compile texts under supervision that are directly comprehensible to the targeted readers - generally specialist-subject teachers at high-school level.
ObjectiveThe aim is for the students
- to familiarise themselves with a new topic by obtaining material and studying the sources, so that they can selectively extend their specialist competence in this way.
- to independently develop a text on the topic, with special focus on its mathematical comprehensibility in respect of the level of knowledge of the targeted readership.
- To try out different options for specialist further training in their profession.
ContentThematische Schwerpunkte:
Die mentorierte Arbeit in FV besteht in der Regel in einer Literaturarbeit über ein Thema, das einen Bezug zum gymnasialem Unterricht oder seiner Weiterentwicklung hat. Die Studierenden setzen darin Erkenntnisse aus den Vorlesungen in FV praktisch um.

Lernformen:
Alle Studierenden erhalten ein individuelles Thema und erstellen dazu eine eigenständige Arbeit. Sie werden dabei von ihrer Betreuungsperson begleitet. Gegebenenfalls stellen sie ihre Arbeit oder Aspekte daraus in einem Kurzvortrag vor. Die mentorierte
Arbeit ist Teil des Portfolios der Studierenden.
Lecture notesEine Anleitung zur mentorierten Arbeit in FV wird zur Verfügung gestellt.
LiteratureDie Literatur ist themenspezifisch. Sie muss je nach Situation selber beschafft werden oder wird zur Verfügung gestellt.
Prerequisites / NoticeDie Arbeit sollte vor Beginn des Praktikums abgeschlossen werden.
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.
W4 credits2V + 1UH.‑J. Böckenhauer, R. Kralovic
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.
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, D. Kratsch: Exact Exponential Algorithms, 2010.
252-0408-00LCryptographic Protocols Information W5 credits2V + 2UM. Hirt, U. Maurer
AbstractThe 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.
ObjectiveIndroduction to a very active research area with many gems and paradoxical
results. Spark interest in fundamental problems.
ContentThe 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 notesthe 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 / NoticeA 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.
263-2300-00LHow To Write Fast Numerical Code Information Restricted registration - show details
Number of participants limited to 84.

Prerequisite: Master student, solid C programming skills.

Takes place the last time in this form.
W6 credits3V + 2UM. Püschel
AbstractThis course introduces the student to the foundations and state-of-the-art techniques in developing high performance software for mathematical functionality such as matrix operations, transforms, and others. The focus is on optimizing for a single core. This includes optimizing for the memory hierarchy, for special instruction sets, and the possible use of automatic performance tuning.
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.
252-0341-01LInformation Retrieval Information W4 credits2V + 1UG. Fourny
AbstractThis course gives an introduction to information retrieval with a focus on text documents and unstructured data.

Main topics comprise document modelling, various retrieval techniques, indexing techniques, query frameworks, optimization, evaluation and feedback.
ObjectiveWe keep accumulating data at an unprecedented pace, much faster than we can process it. While Big Data techniques contribute solutions accounting for structured or semi-structured shapes such as tables, trees, graphs and cubes, the study of unstructured data is a field of its own: Information Retrieval.

After this course, you will have in-depth understanding of broadly established techniques in order to model, index and query unstructured data (aka, text), including the vector space model, boolean queries, terms, posting lists, dealing with errors and imprecision.

You will know how to make queries faster and how to make queries work on very large datasets. You will be capable of evaluating the quality of an information retrieval engine.

Finally, you will also have knowledge about alternate models (structured data, probabilistic retrieval, language models) as well as basic search algorithms on the web such as Google's PageRank.
Content1. Introduction

2. Boolean retrieval: the basics of how to index and query unstructured data.

3. Term vocabulary: pre-processing the data prior to indexing: building the term vocabulary, posting lists.

4. Tolerant retrieval: dealing with spelling errors: tolerant retrieval.

5. Index construction: scaling up to large datasets.

6. Index compression: how to improve performance by compressing the index in various ways.

7. Ranked retrieval: how to ranking results with scores and the vector space model

8. Scoring in a bigger picture: taking ranked retrieval to the next level with various improvements, including inexact retrieval

9. Probabilistic information retrieval: how to leverage Bayesian techniques to build an alternate, probabilistic model for information retrieval

10. Language models: another alternate model based on languages, automata and document generation

11. Evaluation: precision, recall and various other measurements of quality

12. Web search: PageRank

13. Wrap-up.

The lecture structure will follow the pedagogical approach of the book (see material).

The field of information retrieval also encompasses machine learning aspects. However, we will make a conscious effort to limit overlaps, and be complementary with, the Introduction to Machine Learning lecture.
LiteratureC. D. Manning, P. Raghavan, H. Schütze, Introduction to Information Retrieval, Cambridge University Press.
Prerequisites / NoticePrior knowledge in elementary set theory, logics, linear algebra, data structures, abstract data types, algorithms, and probability theory (at the Bachelor's level) is required, as well as programming skills (we will use Python).
252-1403-00LInvitation to Quantum Informatics Information W3 credits2VS. Wolf
AbstractFollowed by an introduction to the basic principles of quantum physics, such as superposition, interference, or entanglement, a variety of subjects are treated: Quantum algorithms, teleportation, quantum communication complexity and "pseudo-telepathy", quantum cryptography, as well as the main concepts of quantum information theory.
ObjectiveIt is the goal of this course to get familiar with the most important notions that are of importance for the connection between Information and Physics. The formalism of Quantum Physics will be motivated and derived, and the use of these laws for information processing will be understood. In particular, the important algorithms of Grover as well as Shor will be studied and analyzed.
ContentAccording to Landauer, "information is physical". In quantum information, one is interested in the consequences and the possibilites offered by the laws of quantum physics for information processing. Followed by an introduction to the basic principles of quantum physics, such as superposition, interference, or entanglement, a variety of subjects are treated: Quantum algorithms, teleportation, quantum communication complexity and "pseude-telepathy", quantum cryptography, as well as the main concepts of quantum information theory.
Compulsory Elective Courses
Further course offerings from the category Educational Science are listed under "Programme: Educational Science for Teaching Diploma and TC".
» see Compulsory Elective Courses Teaching Diploma
Additional Requirements (ETH-Masterstudents in PHYS/MATH/CSE)
Part 1
NumberTitleTypeECTSHoursLecturers
252-0002-00LData Structures and Algorithms Information O8 credits4V + 2UF. O. Friedrich
AbstractThis course is about fundamental algorithm design paradigms (such as induction, divide-and-conquer, backtracking, dynamic programming), classic algorithmic problems (such as sorting and searching), and data structures (such as lists, hashing, search trees). Moreover, an introduction to parallel programming is provided. The programming model of C++ will be discussed in some depth.
ObjectiveAn understanding of the design and analysis of fundamental algorithms and data structures. Knowledge regarding chances, problems and limits of parallel and concurrent programming. Deeper insight into a modern programming model by means of the programming language C++.
ContentFundamental algorithms and data structures are presented and analyzed. Firstly, this comprises design paradigms for the development of algorithms such as induction, divide-and-conquer, backtracking and dynamic programming and classical algorithmic problems such as searching and sorting. Secondly, data structures for different purposes are presented, such as linked lists, hash tables, balanced search trees, heaps and union-find structures. The relationship and tight coupling between algorithms and data structures is illustrated with geometric problems and graph algorithms.

In the part about parallel programming, parallel architectures are discussed conceptually (multicore, vectorization, pipelining). Parallel programming concepts are presented (Amdahl's and Gustavson's laws, task/data parallelism, scheduling). Problems of concurrency are analyzed (Data races, bad interleavings, memory reordering). Process synchronisation and communication in a shared memory system is explained (mutual exclusion, semaphores, monitors, condition variables). Progress conditions are analysed (freedom from deadlock, starvation, lock- and wait-freedom). The concepts are underpinned with examples of concurrent and parallel programs and with parallel algorithms.

The programming model of C++ is discussed in some depth. The RAII (Resource Allocation is Initialization) principle will be explained. Exception handling, functors and lambda expression and generic prorgamming with templates are further examples of this part. The implementation of parallel and concurrent algorithm with C++ is also part of the exercises (e.g. threads, tasks, mutexes, condition variables, promises and futures).
LiteratureCormen, Leiserson, Rivest, and Stein: Introduction to Algorithms, 3rd ed., MIT Press, 2009. ISBN 978-0-262-03384-8 (recommended text)

Maurice Herlihy, Nir Shavit, The Art of Multiprocessor Programming, Elsevier, 2012.

B. Stroustrup, The C++ Programming Language (4th Edition) Addison-Wesley, 2013.
Prerequisites / NoticePrerequisites:
Lecture Series 252-0835-00L Informatik I or equivalent knowledge in programming with C++.
252-0063-00LData Modelling and Databases Information O7 credits4V + 2UG. Alonso, C. Zhang
AbstractData modelling (Entity Relationship), relational data model, relational design theory (normal forms), SQL, database integrity, transactions and advanced database engines
ObjectiveIntroduction to relational databases and data management. Basics of SQL programming and transaction management.
ContentThe course covers the basic aspects of the design and implementation of databases and information systems. The courses focuses on relational databases as a starting point but will also cover data management issues beyond databases such as: transactional consistency, replication, data warehousing, other data models, as well as SQL.
LiteratureKemper, Eickler: Datenbanksysteme: Eine Einführung. Oldenbourg Verlag, 7. Auflage, 2009.

Garcia-Molina, Ullman, Widom: Database Systems: The Complete Book. Pearson, 2. Auflage, 2008.
Part 2
NumberTitleTypeECTSHoursLecturers
252-0211-00LInformation Security Information W8 credits4V + 3UD. Basin, S. Capkun, E. Mohammadi
AbstractThis course provides an introduction to Information Security. The focus
is on fundamental concepts and models, basic cryptography, protocols and system security, and privacy and data protection. While the emphasis is on foundations, case studies will be given that examine different realizations of these ideas in practice.
ObjectiveMaster fundamental concepts in Information Security and their
application to system building. (See objectives listed below for more details).
Content1. Introduction and Motivation (OBJECTIVE: Broad conceptual overview of information security) Motivation: implications of IT on society/economy, Classical security problems, Approaches to
defining security and security goals, Abstractions, assumptions, and trust, Risk management and the human factor, Course verview. 2. Foundations of Cryptography (OBJECTIVE: Understand basic
cryptographic mechanisms and applications) Introduction, Basic concepts in cryptography: Overview, Types of Security, computational hardness, Abstraction of channel security properties, Symmetric
encryption, Hash functions, Message authentication codes, Public-key distribution, Public-key cryptosystems, Digital signatures, Application case studies, Comparison of encryption at different layers, VPN, SSL, Digital payment systems, blind signatures, e-cash, Time stamping 3. Key Management and Public-key Infrastructures (OBJECTIVE: Understand the basic mechanisms relevant in an Internet context) Key management in distributed systems, Exact characterization of requirements, the role of trust, Public-key Certificates, Public-key Infrastructures, Digital evidence and non-repudiation, Application case studies, Kerberos, X.509, PGP. 4. Security Protocols (OBJECTIVE: Understand network-oriented security, i.e.. how to employ building blocks to secure applications in (open) networks) Introduction, Requirements/properties, Establishing shared secrets, Principal and message origin authentication, Environmental assumptions, Dolev-Yao intruder model and
variants, Illustrative examples, Formal models and reasoning, Trace-based interleaving semantics, Inductive verification, or model-checking for falsification, Techniques for protocol design,
Application case study 1: from Needham-Schroeder Shared-Key to Kerberos, Application case study 2: from DH to IKE. 5. Access Control and Security Policies (OBJECTIVES: Study system-oriented security, i.e., policies, models, and mechanisms) Motivation (relationship to CIA, relationship to Crypto) and examples Concepts: policies versus models versus mechanisms, DAC and MAC, Modeling formalism, Access Control Matrix Model, Roll Based Access Control, Bell-LaPadula, Harrison-Ruzzo-Ullmann, Information flow, Chinese Wall, Biba, Clark-Wilson, System mechanisms: Operating Systems, Hardware Security Features, Reference Monitors, File-system protection, Application case studies 6. Anonymity and Privacy (OBJECTIVE: examine protection goals beyond standard CIA and corresponding mechanisms) Motivation and Definitions, Privacy, policies and policy languages, mechanisms, problems, Anonymity: simple mechanisms (pseudonyms, proxies), Application case studies: mix networks and crowds. 7. Larger application case study: GSM, mobility