Search result: Catalogue data in Autumn Semester 2016

Computational Science and Engineering TC Information
Detailed information on the programme at: Link
Educational Science
General course offerings in the category Educational Science are listed under "Programme: Educational Science for Teaching Diploma and TC".
NumberTitleTypeECTSHoursLecturers
851-0240-00LHuman Learning (EW1)
This lecture is only apt for students who intend to enrol in the programs "Teaching Diploma" or "Teaching Certificate". It is about learning in childhood and adolescence.
O2 credits2GE. Stern
AbstractThis course looks into scientific theories and also empirical
studies on human learning and relates them to the school.
ObjectiveAnyone wishing to be a successful teacher must first of all understand the learning process. Against this background, theories and findings on the way humans process information and on human behaviour are prepared in such a manner that they can be used for planning and conducting lessons. Students additionally gain an understanding of what is going on in learning and behavioural research so that teachers are put in a position where they can further educate themselves in the field of research into teaching and learning.
ContentThematische Schwerpunkte:
Lernen als Verhaltensänderung und als Informationsverarbeitung; Das menschliche Gedächtnis unter besonderer Berücksichtigung der Verarbeitung symbolischer Information; Lernen als Wissenskonstruktion und Kompetenzerwerb unter besonderer Berücksichtigung des Wissenstransfers; Lernen durch Instruktion und Erklärungen; Die Rolle von Emotion und Motivation beim Lernen; Interindividuelle Unterschiede in der Lernfähigkeit und ihre Ursachen: Intelligenztheorien, Geschlechtsunterschiede beim Lernen

Lernformen:
Theorien und wissenschaftliche Konstrukte werden zusammen mit ausgewählten wissenschaftlichen Untersuchungen in Form einer Vorlesung präsentiert. Die Studierenden vertiefen nach jeder Stunde die Inhalte durch die Bearbeitung von Aufträgen in einem elektronischen Lerntagebuch. Über die Bedeutung des Gelernten für den Schulalltag soll reflektiert werden. Ausgewählte Tagebucheinträge werden zu Beginn jeder Vorlesung thematisiert.
Lecture notesFolien werden zur Verfügung gestellt.
Literature1) Marcus Hasselhorn & Andreas Gold (2006). Pädagogische Psychologie: Erfolgreiches Lernen und Lehren. Stuttgart: Kohlhammer. 2) Jeanne Omrod (2006): Human Learning. Upper Saddle River: Pearson Prentice Hall.
Prerequisites / NoticeThis lecture is only apt for students who intend to enrol in the programs "Lehrdiplom" or "Didaktisches Zertifikat". It is about learning in childhood and adolescence.
851-0240-03LIntroduction to Test Theory and Test Construction in Educational Contexts (University of Zürich)
Enrolment only possible with Teaching Diploma or DC matriculation.

No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: 200a968

Mind the enrolment deadlines at UZH:
Link
W4 credits2SUniversity lecturers
AbstractIn this seminar, students establish the scientific fundamentals of performance measurement and educational diagnostics and study them on the basis of different current issues.
ObjectiveAt the end of the seminar, participants will be in a position to
- describe the scientific fundamentals of test theory and test structure.
- evaluate examples of scientifically-developed tests in their application context.
- if necessary, critically question the performance assessment that they employ in practice and professionalise it still further.
ContentDie konkreten Inhalte des Seminars ergeben sich aufgrund der Präferenzen der Teilnehmenden und der daraus abgeleiteten Themenübersicht für Vorträge und Seminararbeiten. Im Rahmen der Startveranstaltung wird eine Liste mit möglichen Themen abgegeben und erläutert. Schwerpunkte der Themenvorschläge sind:
- Testentwicklung
- Gütekriterien von Tests
- Aufgabenkonstruktion
- Datenauswertung
- Rasch-Modell
- Internationale Vergleichstests
- Zulassungstests
Lecture notesIm Verlaufe des Semesters werden einzelne Unterlagen in den Veranstaltungen abgegeben. Dazu gehören auch die Handouts der verschiedenen, studentischen Vorträge.
LiteratureAls Grundlagenliteratur werden folgende Werke empfohlen:
- Rost, J. (2004). Lehrbuch Testtheorie - Testkonstruktion (2. Aufl.). Bern: Huber
- Weitere Literatur wird in der Lehrveranstaltung genannt.
Prerequisites / NoticeDie Leistungsanforderungen richten sich im Umfang nach der Zahl zu erwerbender ECTS-Punkte, wobei 1 ECTS-Punkt einem Zeitaufwand von ca. 30 Arbeitsstunden entspricht. ETHZ-Studierende können im Rahmen dieser Veranstaltung 3 ECTS-Punkte erwerben.
Dazu sind folgende Leistungen zu erbringen:
- Präsenz und aktive mündliche Mitarbeit in der Lehrveranstaltung (MA)
- Pflichtlektüre entsprechend der Angaben in der Lehrveranstaltung
- Referat (RE)
- Schreiben einer schriftlichen Arbeit

Weitere Angaben zu den Leistungsanforderungen werden im Rahmen der Startveranstaltung abgegeben und erläutert.
851-0240-16LColloquium on the Science of Learning and InstructionW1 credit1KE. Stern, P. Greutmann, further lecturers
AbstractIn the colloquium we discuss scientific projects concerning the teaching in mathematics, computer science, natural sciences and technology (STEM). The colloquium is conducted by the professorships participating in the Competence Center EducETH (ETH) and in the Institute for Educational Sciences (UZH).
ObjectiveParticipants are exemplarily introduced to different research methods used in research on learning and instruction and learn to weigh advantages and disadvantages of these approaches.
851-0240-22LCoping with Psychosocial Demands of Teaching (EW4 DZ) Restricted registration - show details
Number of participants limited to 20.
The successful participation in EW1 ("Human Learning") and EW2 ("Designing Learning Environments for School") is recommended, but not a mandatory prerequisite.
W2 credits3SA. Deiglmayr, P. Greutmann, U. Markwalder
AbstractIn this class, students will learn concepts and skills for coping with psychosocial demands of teaching
ObjectiveStudents possess theoretical knowledge and practical competences to be able to cope with the psychosocial demands of teaching.

(1) They know the basic rules of negotiation and conflict management (e.g., mediation) and can apply them in the school context (e.g., in conversations with parents).
(2) They can apply diverse techniques of classroom management (e.g., prevention of disciplinary problems in the classroom) and know relevant authorities for further information (e.g., legal conditions).
851-0242-06LCognitively Activating Instructions in MINT Subjects Restricted registration - show details
Enrolment only possible with matriculation in Teaching Diploma or Teaching Certificate (excluding Teaching Diploma Sport).

This course unit can only be enrolled after successful participation in, or during enrollment in the course "Human Learning (EW 1)".
W2 credits2SR. Schumacher
AbstractThis 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 / NoticeFür eine reibungslose Semesterplanung wird um frühe Anmeldung und persönliches Erscheinen zum ersten Lehrveranstaltungstermin ersucht.
851-0242-08LResearch Methods in Educational Science Restricted registration - show details
Number of participants limited to 30
This course unit can only be enrolled after successful participation in, or during enrollment in the course "Human Learning (EW 1)".
W1 credit1SP. Edelsbrunner, B. Rütsche, E. Stern, E. Ziegler
AbstractLiterature from the learning sciences is critically discussed with a focus on research methods.
At the first meeting, working groups will be assembled and meetings with those will be set up.
In the small groups students will write critical essays about the read literature. At the third meeting, we will discuss the essays and develop research questions in group work.
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
851-0242-07LHuman Intelligence Restricted registration - show details
Enrolment only possible with matriculation in Teaching Diploma or Teaching Certificate (excluding Teaching Diploma Sport).
Number of participants limited to 30.
This course unit can only be enrolled after successful participation in, or during enrollment in the course "Human Learning (EW 1)".
W1 credit1SE. Stern, P. Edelsbrunner, B. Rütsche
AbstractThe 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
Subject Didactics and Professional Training
NumberTitleTypeECTSHoursLecturers
401-9908-00LTeaching Internship Including Examination Lessons Computational Science and Engineering Restricted registration - show details
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.
W6 credits13PJ. Hromkovic, G. Serafini
AbstractStudents 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.
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 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 notesDokument: schriftliche Vorbereitung für Prüfungslektionen.
LiteratureWird von der Praktikumslehrperson bestimmt.
272-0101-00LSubject Didactics of Computer Science I Restricted registration - show details
Simultaneous enrolment in Introductory Practical in Computer Science - course 272-0201-00L - is compulsory.
O4 credits3GG. Serafini, J. Hromkovic
AbstractThe unit "Subject Didactics of Computer Science I" addresses key contributions of computer science to general education. The course deals with the thoughtful choice of educational contents for computer science classes, which takes into account its comprehensibility for different age groups as well as didactic approaches suitable for a successful knowledge transfer.
ObjectiveThe general objective of the course consists in highlighting the tight connection between the mathematical and algorithmic way of thinking and the approaches adopted by engineering disciplines, and in reflecting on teaching approaches for sustainable computer science teaching activities.

The students understand the fundamental 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 as well as their advantages and disadvantages. They can handle inhomogeneous prior knowledge of the learners inside a class. Besides holding classes, the students do care about the individual pupil support.

They encourage the autonomy of the learners, manage to work with diverse target groups and to establish a positive learning environment.

The students are able to express themselves using a comprehensible and refined professional language, both in a spoken and a written way, and they master the basic terminology of computer science. Besides the English terms, they are familiar with the corresponding German expressions. The students are able to produce detailed, matured, linguistically correct and design-wise appealing teaching materials.
ContentThe course "Subject Didactics of Computer Science I" addresses key contributions of computer science to general education. The chosen topics support the young learners in developing a unique and indispensable way of thinking, in enhancing their understanding of our world as well as in achieving university education entrance qualifications.

The main topics of the course unit "Subject Didactics of Computer Science I" are the didactics of finite state automata, of formal languages and of the introduction to programming. The unit focuses on contents of computer science that contribute to general education. This involves the understanding of fundamental scientific concepts such as algorithm, complexity, determinism, computation, automata, verification, testing and programming language as well as the way to embed them into a scientifically sound and didactically sustainable computer science course.

In a semester exercise, the students develop and document an adaptive teaching unit for computer science. They learn to employ the didactics methods and techniques that are introduced at the beginning of the semester.
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).

J. Hromkovic: Einführung in die Programmierung mit LOGO: Lehrbuch für Unterricht und Selbststudium. Springer Vieweg; Auflage: 3 (2014)
Prerequisites / NoticeLehrdiplom-Studierende müssen diese Lerneinheit zusammen mit dem Einführungspraktikum Informatik - 272-0201-00L - belegen.
401-9901-00LMentored Work Subject Didactics Computational Science and Engineering Information Restricted registration - show details W2 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.
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.
Further Subject Didactics
NumberTitleTypeECTSHoursLecturers
263-2800-00LDesign of Parallel and High-Performance Computing Information W7 credits3V + 2U + 1AT. Hoefler, M. Püschel
AbstractAdvanced topics in parallel / concurrent programming.
ObjectiveUnderstand concurrency paradigms and models from a higher perspective and acquire skills for designing, structuring and developing possibly large concurrent software systems. Become able to distinguish parallelism in problem space and in machine space. Become familiar with important technical concepts and with concurrency folklore.
252-0341-01LInformation Retrieval Information W4 credits2V + 1UT. Hofmann
AbstractIntroduction to information retrieval with a focus on text documents and images. Main topics comprise extraction of characteristic features from documents, index structures, retrieval models, search algorithms, benchmarking, and feedback mechanisms. Searching the web, images and XML collections demonstrate recent applications of information retrieval and their implementation.
ObjectiveIn depth understanding of managing, indexing, and retrieving documents with text, image and XML content. Knowledge about basic search algorithms on the web, benchmarking of search algorithms, and relevance feedback methods.
252-0535-00LMachine Learning Information W8 credits3V + 2U + 2AJ. M. Buhmann
AbstractMachine 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.
ObjectiveStudents will be familiarized with the most important 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. A machine learning project will provide an opportunity to test the machine learning algorithms on real world data.
ContentThe 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:

- Bayesian theory of optimal decisions
- Maximum likelihood and Bayesian parameter inference
- Classification with discriminant functions: Perceptrons, Fisher's LDA and support vector machines (SVM)
- Ensemble methods: Bagging and Boosting
- Regression: least squares, ridge and LASSO penalization, non-linear regression and the bias-variance trade-off
- Non parametric density estimation: Parzen windows, nearest nieghbour
- Dimension reduction: principal component analysis (PCA) and beyond
Lecture notesNo lecture notes, but slides will be made available on the course webpage.
LiteratureC. 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.
Prerequisites / NoticeThe course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments.
Students should at least have followed one previous course offered by the Machine Learning Institute (e.g., CIL or LIS) or an equivalent course offered by another institution.
252-1407-00LAlgorithmic Game Theory Information W7 credits3V + 2U + 1AP. Widmayer, P. Penna
AbstractGame theory provides a formal model to study the behavior and interaction of self-interested users and programs in large-scale distributed computer systems without central control. The course discusses algorithmic aspects of game theory.
ObjectiveLearning the basic concepts of game theory and mechanism design, acquiring the computational paradigm of self-interested agents, and using these concepts in the computational and algorithmic setting.
ContentThe Internet is a typical example of a large-scale distributed computer system without central control, with users that are typically only interested in their own good. For instance, they are interested in getting high bandwidth for themselves, but don't care about others, and the same is true for computational load or download rates. Game theory provides a particularly well-suited model for the behavior and interaction of such selfish users and programs. Classic game theory dates back to the 1930s and typically does not consider algorithmic aspects at all. Only a few years back, algorithms and game theory have been considered together, in an attempt to reconcile selfish behavior of independent agents with the common good.

This course discusses algorithmic aspects of game-theoretic models, with a focus on recent algorithmic and mathematical developments. Rather than giving an overview of such developments, the course aims to study selected important topics in depth.

Outline:
- Introduction to classic game-theoretic concepts.
- Existence of stable solutions (equilibria), algorithms for computing equilibria, computational complexity.
- Speed of convergence of natural game playing dynamics such as best-response dynamics or regret minimization.
- Techniques for bounding the quality-loss due to selfish behavior versus optimal outcomes under central control (a.k.a. the 'Price of Anarchy').
- Design and analysis of mechanisms that induce truthful behavior or near-optimal outcomes at equilibrium.
- Selected current research topics, such as Google's Sponsored Search Auction, the U.S. FCC Spectrum Auction, Kidney Exchange.
Lecture notesNo lecture notes.
Literature"Algorithmic Game Theory", edited by N. Nisan, T. Roughgarden, E. Tardos, and V. Vazirani, Cambridge University Press, 2008;

"Game Theory and Strategy", Philip D. Straffin, The Mathematical Association of America, 5th printing, 2004

Several copies of both books are available in the Computer Science library.
Prerequisites / NoticeAudience: Although this is a Computer Science course, we encourage the participation from all students who are interested in this topic.

Requirements: You should enjoy precise mathematical reasoning. You need to have passed a course on algorithms and complexity. No knowledge of game theory is required.
252-0417-00LRandomized Algorithms and Probabilistic MethodsW7 credits3V + 2U + 1AA. Steger, E. Welzl
AbstractLas Vegas & Monte Carlo algorithms; inequalities of Markov, Chebyshev, Chernoff; negative correlation; Markov chains: convergence, rapidly mixing; generating functions; Examples include: min cut, median, balls and bins, routing in hypercubes, 3SAT, card shuffling, random walks
ObjectiveAfter this course students will know fundamental techniques from probabilistic combinatorics for designing randomized algorithms and will be able to apply them to solve typical problems in these areas.
ContentRandomized Algorithms are algorithms that "flip coins" to take certain decisions. This concept extends the classical model of deterministic algorithms and has become very popular and useful within the last twenty years. In many cases, randomized algorithms are faster, simpler or just more elegant than deterministic ones. In the course, we will discuss basic principles and techniques and derive from them a number of randomized methods for problems in different areas.
Lecture notesYes.
Literature- Randomized Algorithms, Rajeev Motwani and Prabhakar Raghavan, Cambridge University Press (1995)
- Probability and Computing, Michael Mitzenmacher and Eli Upfal, Cambridge University Press (2005)
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