This course introduces the student to the foundations and state-of-the-art techniques in developing high performance software for mathematical functionality occurring in various fields in computer science. The focus is on optimizing for a single core and includes optimizing for the memory hierarchy, for special instruction sets, and the possible use of automatic performance tuning.

This module will cover basic theoretical knowledge on visualrecognition systems of the last two decades, mostly focusing on themost recent advancements in deep learning and convolutional neuralnetworks.

The seminar covers core concepts and ideas in the general area of computer systems, ranging from software and hardware architectures to system design for operating systems, data processing systems, and distributed systems.

Data modelling (Entity Relationship), relational data model, relational design theory (normal forms), SQL, database integrity, transactions and advanced database engines

In this class, we bring together data science applicationsprovided by ETH researchers outside computer science andteams of computer science master's students. Two to threestudents will form a team working on data science/machinelearning-related research topics provided by scientists ina diverse range of domains such as astronomy, biology,social sciences etc.

This 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 mobile phones.

The seminar will cover topics related to data processing using new hardware in general and hardware accelerators (GPU, FPGA, specialized processors) in particular.

Large language models have become one of the most commonly deployed NLP inventions. In the past half-decade, their integration into core natural language processing tools has dramatically increased the performance of such tools, and they have entered the public discourse surrounding artificial intelligence.

This 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.