In this module, basic paradigms and techniques in working with data will be discussed, especially towards data security, managing data decentrally, and learning from data.
Lernziel
Participants learn about some important computer science concepts necessary for data science. They understand some of these concepts in detail and see the mathematics behind them.
Inhalt
Participants will get an introduction to key computer science concepts underlying current and upcoming technology. The module in particular covers cryptography and digital signatures, networking and distributed algorithms, distributed ledger technology, as well as machine learning (supervised and unsupervised learning). Each topic will be discussed in two different ways: (i) a hands-on and in-depth introduction that allows participants to gain a technical understanding of key ideas. This is supported by simple and concrete examples as well as programming assignments; (ii) a context part that addresses the challenges and limitations encountered in practical applications.
Leistungskontrolle
Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Repetition ohne erneute Belegung der Lerneinheit möglich.
Zusatzinformation zum Prüfungsmodus
Two homeworks and a written exam (90 minutes). Each homework as well as the exam are graded. If H1, H2 and E are the homework and exam performances (measured in the percentage of points achieved), the module performance is 0.1*H1 + 0.1*H2 + 0.8*E. This means, each homework contributes 10%, and the exam contributes 80%.
A module performance of 50% or higher is guaranteed to be a passing performance, but depending on the cohort, we may also require less than 50% to pass the module.
In case of a failing module performance, the exam can be repeated in the form of a 30 minute oral exam, and the result will replace the performance E. If this again results in a failing module performance, the module cannot be passed anymore.
The final exam has been scheduled for November 13, 2021, 8-12, room HG D7.2!
Lernmaterialien
Keine öffentlichen Lernmaterialien verfügbar.
Es werden nur die öffentlichen Lernmaterialien aufgeführt.
Gruppen
Keine Informationen zu Gruppen vorhanden.
Einschränkungen
Vorrang
Die Belegung der Lerneinheit ist nur durch die primäre Zielgruppe möglich
Primäre Zielgruppe
MAS ETH in Applied Technology (247000)
CAS ETH in Applied ML & Information Processing (265000)
Leistungskontrolle als Semesterkurs