Ce Zhang: Catalogue data in Autumn Semester 2022

Name Dr. Ce Zhang
URLhttps://zhangce.github.io/
DepartmentComputer Science
RelationshipAssociate Professor

NumberTitleECTSHoursLecturers
252-0817-00LDistributed Systems Laboratory Information 10 credits9PG. Alonso, T. Hoefler, A. Klimovic, T. Roscoe, R. Wattenhofer, C. Zhang
AbstractThis course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including wireless networks, ad-hoc networks, RFID, and distributed applications on smartphones.
ObjectiveGain hands-on-experience with real products and the latest technology in distributed systems.
ContentThis course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including as well wireless networks, ad-hoc networks, and distributed application on smartphones. The goal of the project is for the students to gain hands-on-experience with real products and the latest technology in distributed systems. There is no lecture associated to the course.
252-3400-00LSeminar on Machine Learning Systems Information Restricted registration - show details
Number of participants limited to 40.

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 credits2SA. Klimovic, C. Zhang
AbstractThis seminar covers core concepts and ideas in the general area of machine learning systems, ranging from distributed and federated learning systems, DevOps systems for ML, life cycle and data management systems for ML, etc.
ObjectiveThe seminar covers core concepts and ideas in the general area of machine learning systems, ranging from distributed and federated learning systems, DevOps systems for ML, life cycle and data management systems for MLs, etc. The focus will be to cover fundamental ideas on ML systems, with an emphasis on software systems and platforms.
ContentThe seminar will consist of student presentations based on a list of papers that will be provided at the beginning of the course. Presentations will be done in teams. Presentations will be arranged in slots of 30 minutes talk plus 15 minutes questions. Grades will be assigned based on quality of the presentation, coverage of the topic including material not in the original papers, participation during the seminar, and ability to understand, present, and criticize the underlying technology.
263-3300-00LData Science Lab Information Restricted registration - show details
Only for Data Science MSc.
14 credits9PC. Zhang, V. Boeva, R. Cotterell, A. Ilic, J. Vogt, F. Yang
AbstractIn this class, we bring together data science applications
provided by ETH researchers outside computer science and
teams of computer science master's students. Two to three
students will form a team working on data science/machine
learning-related research topics provided by scientists in
a diverse range of domains such as astronomy, biology,
social sciences etc.
ObjectiveThe goal of this class if for students to gain experience
of dealing with data science and machine learning applications
"in the wild". Students are expected to go through the full
process starting from data cleaning, modeling, execution,
debugging, error analysis, and quality/performance refinement.
Prerequisites / NoticePrerequisites: At least 8 KP must have been obtained under Data Analysis and at least 8 KP must have been obtained under Data Management and Processing.
265-0102-00LData Modeling and Computer Vision Restricted registration - show details
Only for CAS in Applied Information Technology and MAS in Applied Technology.
3 credits2VE. Konukoglu, C. Zhang
AbstractThis module offers practical knowledge in visual information processing and human computer interactions.
ObjectiveParticipants understand basic concepts of visual regonition and human-computer interaction systems.
ContentThe first part of the module will cover basic theoretical knowledge on visual recognition systems of the last two decades, mostly focusing on the most recent advancements in deep learning and convolutional neural networks. The theoretical knowledge will be supported with practical sessions that will allow participants to gain hands-on experience with most commonly used tools and deepen their understanding of the key concepts. The second part provides an introduction to the field of human-computer interaction, emphasising the central role of the user in system design. Through detailed case studies, students will be introduced to different methods used to analyse the user experience and shown how these can inform the design of new interfaces, systems and technologies.