Search result: Catalogue data in Spring Semester 2018

Number	Title	Type	ECTS	Hours	Lecturers
Computational Science and Engineering TC Detailed information on the programme at: www.didaktischeausbildung.ethz.ch
Educational Science General course offerings in the category Educational Science are listed under "Programme: Educational Science for Teaching Diploma and TC".
851-0240-17L	Designing Learning Environments for School: Educational Foundations (EW2 TC) - Prerequisite: successful participation in 851-0240-00L "Human Learning (EW1)". - Addresses to students enrolled in "Teaching Certificate in a non-college Discipline (TC)". - The simultaneous enrolment in course 851-0240-25 Designing Learning Environments for School: Vocational Education (EW2 TC)" is recommended, but not a mandatory prerequisite.	O	2 credits	1G	A. Deiglmayr, P. Edelsbrunner
Abstract	Teaching is also a craft. In this lecture, students get to know and, wherever possible, also practice practical aspects of the teaching profession within the framework of relevant theories rom the Learning Sciences.
Objective	Students acquire basic knowledge and skills needed for planning, preparing, and implementing effective instruction. They can reflect and adapt these skills based on knowledge about findings from research in the learning sciences.
Content	We discuss characteristics of successful lessons and how to design such lessons by using curricula and lesson plans, teaching goals, classroom management, and a variety of teaching methods.
Lecture notes	The 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".
Literature	The necessary literature can be downloaded from "Moodle".
Prerequisites / Notice	The 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.
851-0240-25L	Designing Learning Environments for School: Vocational Education (EW2 TC) - Prerequisite: successful participation in 851-0240-00L "Human Learning (EW1)". - Addresses to students enrolled in "Teaching Certificate in a non-college Discipline (TC)". - The simultaneous enrolment in course 851-0240-17L Designing Learning Environments for School: Educational Foundations (EW2 DZ)" is recommended, but not a mandatory prerequisite.	O	2 credits	1G	G. Kaufmann
Abstract	Participants acquire knowledge in vocational training system and in theory and practice of vocational education. They get to know characteristics of functions, tasks and roles in the professional world. They deduce consequences for the planning and execution of learner-tailored and effective learning in vocational education taking into account the theory and practice of vocational education.
Objective	Participants would be able to structure and execute learner-tailored and effective learning in vocational education taking into account the theory and practice of vocational education.
851-0242-03L	Introduction to General Pedagogy Enrolment only possible with matriculation in Teaching Diploma or Teaching Certificate. Prerequisite: successful participation in 851-0240-00L "Human Learning (EW1)".	W	2 credits	2G	L. Haag
Abstract	The basics of educational science and the field of activity of the school are conveyed in as much as they are of relevance to the field of activity of the teachers. Basic knowledge is taught methodically by the lecturers which is further deepened by the reading of selected texts and corresponding work assignments in individual and small groups.
Objective	1. Basics of educational science 1.1 Historical survey of education and school 1.2 Fundamental educational terms - Education as field of activity of the school - Education at school - Socialization 2. Field of activity of the school 2.1 Theory of school - Theory of school - Curriculum theory - School development 2.2 Theory of instruction - Didactic analysis - Principles of learning - Handling of heterogeneity
851-0242-06L	Cognitively Activating Instructions in MINT Subjects Enrolment only possible with matriculation in Teaching Diploma or Teaching Certificate (excluding Teaching Diploma Sport). This course unit can only be enroled after successful participation in, or during enrolment in the course 851-0240-00L "Human Learning (EW 1)".	W	2 credits	2S	R. Schumacher
Abstract	This seminar focuses on teaching units in chemistry, physics and mathematics that have been developed at the MINT Learning Center of the ETH Zurich. In the first meeting, the mission of the MINT Learning Center will be communicated. Furthermore, in groups of two, the students will intensively work on, refine and optimize a teaching unit following a goal set in advance.
Objective	- Get to know cognitively activating instructions in MINT subjects - Get information about recent literature on learning and instruction
Prerequisites / Notice	Für eine reibungslose Semesterplanung wird um frühe Anmeldung und persönliches Erscheinen zum ersten Lehrveranstaltungstermin ersucht.
851-0242-07L	Human Intelligence Number of participants limited to 30. Enrolment only possible with matriculation in Teaching Diploma or Teaching Certificate (excluding Teaching Diploma Sport). This course unit can only be enroled after successful participation in, or imultaneous enrolment in the course 851-0240-00L "Human Learning (EW 1)" .	W	1 credit	1S	E. Stern, Z. Lue
Abstract	The focus will be on the book "Intelligenz: Grosse Unterschiede und ihre Folgen" by Stern and Neubauer. Participation at the first meeting is obligatory. It is required that all participants read the complete book. Furthermore, in two meetings of 90 minutes, concept papers developed in small groups (5 - 10 students) will be discussed.
Objective	- Understanding of research methods used in the empirical human sciences - Getting to know intelligence tests - Understanding findings relevant for education
851-0242-08L	Research Methods in Educational Science 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)" .	W	1 credit	1S	P. Edelsbrunner, M. Berkowitz Biran, Z. Lue, B. Rütsche
Abstract	Literature 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 TC

Subject Didactics and Professional Training Important: You can only enrol in the courses of this category if you have not more than 12 CP left for possible additional requirements.
Number	Title	Type	ECTS	Hours	Lecturers
401-9908-00L	Teaching Internship Including Examination Lessons Computational Science and Engineering Teaching Internship Computational Science and Engineering for TC. Only for students who enrolled from HS 2011 on into TC. The teaching internship can just be visited if all other courses of TC are completed. Repetition of the teaching internship is excluded even if the examination lessons are to be repeated.	W	6 credits	13P	J. Hromkovic, G. Serafini
Abstract	Students apply the insights, abilities and skills they have acquired within the context of an educational institution. They observe 10 lessons and teach 20 lessons independently. Two of them are as assessed as Examination Lessons.
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 for their subject from different angles (including interdisciplinary angles) and impart these to their pupils. - They learn 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.
Content	Die 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 Themen für die beiden Prüfungslektionen am Schluss des Praktikums erfahren die Studierenden in der Regel eine Woche vor dem Prüfungstermin. Sie erstellen eine Vorbereitung gemäss Anleitung und reichen sie bis am Vortrag um 12 Uhr den beiden Prüfungsexperten (Fachdidaktiker/-in, Departementsvertreter/-in) ein. Die gehaltenen Lektionen werden kriteriumsbasiert beurteilt. Die Beurteilung umfasst auch die schriftliche Vorbereitung und eine mündliche Reflexion des Kandidaten/der Kandidatin über die gehaltenen Lektionen im Rahmen eines kurzen Kolloquiums.
Lecture notes	Dokument: schriftliche Vorbereitung für Prüfungslektionen.
Literature	Wird von der Praktikumslehrperson bestimmt.
401-9901-00L	Mentored Work Subject Didactics Computational Science and Engineering	W	2 credits	4A	J. Hromkovic, G. Serafini
Abstract	In 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.
Objective	The 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.
Content	Thematische 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.
Literature	Die 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 / Notice	Die Arbeit sollte vor Beginn des Praktikums abgeschlossen werden.

Further Subject Didactics
Number	Title	Type	ECTS	Hours	Lecturers
272-0300-00L	Algorithmics for Hard Problems 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 A.	W	4 credits	2V + 1U	J. Hromkovic
Abstract	This course unit looks into algorithmic approaches to the solving of hard problems. The seminar is accompanied by a comprehensive reflection upon the significance of the approaches presented for computer science tuition at high schools.
Objective	To systematically acquire an overview of the methods for solving hard problems.
Content	First, 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.
Lecture notes	Unterlagen und Folien werden zur Verfügung gestellt.
Literature	J. 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.
272-0302-00L	Approximation and Online Algorithms	W	4 credits	2V + 1U	H.‑J. Böckenhauer, D. Komm
Abstract	This lecture deals with approximative algorithms for hard optimization problems and algorithmic approaches for solving online problems as well as the limits of these approaches.
Objective	Get 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.
Content	Approximation 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.
Literature	The 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
272-0301-00L	Methods for Design of Random Systems This course d o e s n o t include the Mentored Work Specialised Courses with an Educational Focus in Computer Science B.	W	4 credits	2V + 1U	H.‑J. Böckenhauer, D. Komm, R. Kralovic
Abstract	The 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.
Objective	To understand the computational power of randomness and to learn the basic methods for designing randomized algorithms
Lecture notes	J. Hromkovic: Randomisierte Algorithmen, Teubner 2004. J.Hromkovic: Design and Analysis of Randomized Algorithms. Springer 2006. J.Hromkovic: Algorithmics for Hard Problems, Springer 2004.
Literature	J. Hromkovic: Randomisierte Algorithmen, Teubner 2004. J.Hromkovic: Design and Analysis of Randomized Algorithms. Springer 2006. J.Hromkovic: Algorithmics for Hard Problems, Springer 2004.
252-0408-00L	Cryptographic Protocols	W	5 credits	2V + 2U	M. Hirt, U. Maurer
Abstract	The course presents a selection of hot research topics in cryptography. The choice of topics varies and may include provable security, interactive proofs, zero-knowledge protocols, secret sharing, secure multi-party computation, e-voting, etc.
Objective	Indroduction to a very active research area with many gems and paradoxical results. Spark interest in fundamental problems.
Content	The course presents a selection of hot research topics in cryptography. The choice of topics varies and may include provable security, interactive proofs, zero-knowledge protocols, secret sharing, secure multi-party computation, e-voting, etc.
Lecture notes	the lecture notes are in German, but they are not required as the entire course material is documented also in other course material (in english).
Prerequisites / Notice	A basic understanding of fundamental cryptographic concepts (as taught for example in the course Information Security or in the course Cryptography Foundations) is useful, but not required.
263-2300-00L	How To Write Fast Numerical Code Does not take place this semester. Number of participants limited to 84. Prerequisite: Master student, solid C programming skills.	W	6 credits	3V + 2U	M. Püschel
Abstract	This course introduces the student to the foundations and state-of-the-art techniques in developing high performance software for numerical functionality such as linear algebra and others. The focus is on optimizing for the memory hierarchy and for special instruction sets. Finally, the course will introduce the recent field of automatic performance tuning.
Objective	Software performance (i.e., runtime) arises through the interaction of algorithm, its implementation, and the microarchitecture the program is run on. The first goal of the course is to provide the student with an understanding of this interaction, and hence software performance, focusing on numerical or mathematical functionality. The second goal is to teach a general systematic strategy how to use this knowledge to write fast software for numerical problems. This strategy will be trained in a few homeworks and semester-long group projects.
Content	The 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 software development using important functionality such as linear algebra functionality, transforms, filters, and others as examples. The course will explain how to optimize for the memory hierarchy, take advantage of special instruction sets, and, if time permits, how to write multithreaded code for multicore platforms. Much of the material is based on state-of-the-art research. Further, a general strategy for performance analysis and optimization is introduced that the students will apply in group projects that accompany the course. Finally, the course will introduce the students to the recent field of automatic performance tuning.

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