263-2926-00L Deep Learning for Big Code
|Periodizität||jährlich wiederkehrende Veranstaltung|
|Kommentar||Number of participants limited to 24.|
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.
|263-2926-00 S||Deep Learning for Big Code||2 Std.|
|Kurzbeschreibung||The seminar covers some of the latest and most exciting developments (industrial and research) in the field of Deep Learning for Code, including new methods and latest systems, as well as open challenges and opportunities.|
|Lernziel||The objective of the seminar is to:|
- Introduce students to the field of Deep Learning for Big Code.
- Learn how machine learning models can be used to solve practical challenges in software engineering and programming beyond traditional methods.
- Highlight the latest research and work opportunities in industry and academia available on this topic.
|Inhalt||The last 5 years have seen increased interest in applying advanced machine learning techniques such as deep learning to new kind of data: program code. As the size of open source code increases dramatically (over 980 billion lines of code written by humans), so comes the opportunity for new kind of deep probabilistic methods and commercial systems that leverage this data to revolutionize software creation and address hard problems not previously possible. Examples include: machines writing code, program de-obfuscation for security, code search, and many more. |
Interestingly, this new type of data, unlike natural language and images, introduces technical challenges not typically encountered when working with standard datasets (e.g., images, videos, natural language), for instance, finding the right representation over which deep learning operates. This in turn has the potential to drive new kinds of machine learning models with broad applicability.
Because of this, there has been substantial interest over the last few years in both industry (e.g., companies such as Facebook starting, various start-ups in the space such as http://deepcode.ai), academia (e.g., http://plml.ethz.ch) and government agencies (e.g., DARPA) on using machine learning to automate various programming tasks.
In this seminar, we will cover some of the latest and most exciting developments in the field of Deep Learning for Code, including new methods and latest systems, as well as open challenges and opportunities.
The seminar is carried out as a set of presentations chosen from a list of available papers. The grade is determined as a function of the presentation, handling questions and answers, and participation.
|Voraussetzungen / Besonderes||The seminar is carried out as a set of presentations chosen from a list of available papers. The grade is determined as a function of the presentation, handling questions and answers, and participation.|
The seminar is ideally suited for M.Sc. students in Computer Science.
|Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)|
|Leistungskontrolle als Semesterkurs|
|ECTS Kreditpunkte||2 KP|
|Repetition||Repetition nur nach erneuter Belegung der Lerneinheit möglich.|
|Zusatzinformation zum Prüfungsmodus||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.|
|Es werden nur die öffentlichen Lernmaterialien aufgeführt.|
|Keine Informationen zu Gruppen vorhanden.|
|Vorrang||Die Belegung der Lerneinheit ist nur durch die primäre Zielgruppe möglich|
|Primäre Zielgruppe||Informatik MSc (263000)|
|Informatik Master||Seminar in General Studies||W|
|Informatik Master||Seminar in Software Engineering||W|