Menna El-Assady: Katalogdaten im Herbstsemester 2023

NameFrau Prof. Dr. Menna El-Assady
NamensvariantenMennatallah El-Assady
LehrgebietInformatik
Adresse
Institut für Visual Computing
ETH Zürich, STF F 112
Stampfenbachstrasse 114
8092 Zürich
SWITZERLAND
E-Mailmenna.elassady@ai.ethz.ch
URLhttps://el-assady.com/
DepartementInformatik
BeziehungAssistenzprofessorin (Tenure Track)

NummerTitelECTSUmfangDozierende
252-2810-00LFundamentals of Web Engineering Belegung eingeschränkt - Details anzeigen 5 KP2V + 2UM. El-Assady, D. Sichau
KurzbeschreibungContemporary web development utilizes a technology stack that spans from back-ends to front-ends, and includes virtual server environments, document databases, back-end and front-end programming, and UI/UX design. The depth of this stack fosters separation of concern
and reuse, but also amounts to a steep learning curve.
LernzielThis course introduces both theoretical and applied aspects of web engineering. It covers:

- DOM, CSS, Typescript
- Fronted and backend frameworks
- Client-server communication
- Interaction design, visualization and narrative storytelling
- Security for in the context of web engineering
- Desktop applications using web development techniques
InhaltThe course has two main objectives:

- Obtain an end-to-end (both, theoretical and practical) understanding of the foundations of web engineering.
- Be able to apply these techniques in practice.

While the lecture will provide the theoretical foundations for the various aspects of web engineering, the students will apply those techniques in project work that will span over the whole semester - involving different aspects of web engineering.
SkriptThe lecture slides are available for download on the course page.
Voraussetzungen / BesonderesTo contact us please us the following email: web-foundations@ethz.ch


Students should be familiar with the basics of a programming language (C, C++, Python, Java, Javascript, Typescript). The course will not teach basics of programming.
KompetenzenKompetenzen
Fachspezifische KompetenzenKonzepte und Theoriengeprüft
Verfahren und Technologiengeprüft
Methodenspezifische KompetenzenAnalytische Kompetenzengefördert
Entscheidungsfindunggefördert
Medien und digitale Technologiengeprüft
Problemlösunggeprüft
Projektmanagementgeprüft
Soziale KompetenzenKommunikationgeprüft
Kooperation und Teamarbeitgeprüft
Persönliche KompetenzenAnpassung und Flexibilitätgeprüft
Kreatives Denkengeprüft
Selbstbewusstsein und Selbstreflexion geprüft
252-5051-00LAdvanced Topics in Machine Learning Belegung eingeschränkt - Details anzeigen
The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
2 KP2SJ. M. Buhmann, R. Cotterell, M. El-Assady, N. He, F. Yang
KurzbeschreibungIn this seminar, recent papers of the pattern recognition and machine learning literature are presented and discussed. Possible topics cover statistical models in computer vision, graphical models and machine learning.
LernzielThe seminar "Advanced Topics in Machine Learning" familiarizes students with recent developments in pattern recognition and machine learning. Original articles have to be presented and critically reviewed. The students will learn how to structure a scientific presentation in English which covers the key ideas of a scientific paper. An important goal of the seminar presentation is to summarize the essential ideas of the paper in sufficient depth while omitting details which are not essential for the understanding of the work. The presentation style will play an important role and should reach the level of professional scientific presentations.
InhaltThe seminar will cover a number of recent papers which have emerged as important contributions to the pattern recognition and machine learning literature. The topics will vary from year to year but they are centered on methodological issues in machine learning like new learning algorithms, ensemble methods or new statistical models for machine learning applications. Frequently, papers are selected from computer vision or bioinformatics - two fields, which relies more and more on machine learning methodology and statistical models.
LiteraturThe papers will be presented in the first session of the seminar.