Menna El-Assady: Catalogue data in Autumn Semester 2023

Name Prof. Dr. Menna El-Assady
Name variantsMennatallah El-Assady
FieldComputer Science
Address
Institut für Visual Computing
ETH Zürich, STF F 112
Stampfenbachstrasse 114
8092 Zürich
SWITZERLAND
E-mailmenna.elassady@ai.ethz.ch
URLhttps://el-assady.com/
DepartmentComputer Science
RelationshipAssistant Professor (Tenure Track)

NumberTitleECTSHoursLecturers
252-2810-00LFundamentals of Web Engineering Restricted registration - show details 5 credits2V + 2UM. El-Assady, D. Sichau
AbstractContemporary 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.
ObjectiveThis 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
ContentThe 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.
Lecture notesThe lecture slides are available for download on the course page.
Prerequisites / NoticeTo 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.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesfostered
Decision-makingfostered
Media and Digital Technologiesassessed
Problem-solvingassessed
Project Managementassessed
Social CompetenciesCommunicationassessed
Cooperation and Teamworkassessed
Personal CompetenciesAdaptability and Flexibilityassessed
Creative Thinkingassessed
Self-awareness and Self-reflection assessed
252-5051-00LAdvanced Topics in Machine Learning Restricted registration - show details
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 credits2SJ. M. Buhmann, R. Cotterell, M. El-Assady, N. He, F. Yang
AbstractIn 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.
ObjectiveThe 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.
ContentThe 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.
LiteratureThe papers will be presented in the first session of the seminar.