Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

263-2926-00L  Deep Learning for Big Code

SemesterAutumn Semester 2018
LecturersV. Raychev
Periodicityyearly recurring course
Language of instructionEnglish
CommentNumber 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.



Catalogue data

AbstractThe 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.
ObjectiveThe 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.
ContentThe 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.
Prerequisites / NoticeThe 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.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits2 credits
ExaminersV. Raychev
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.
Additional information on mode of examinationThe 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.

Learning materials

 
Main linkInformation
Only public learning materials are listed.

Courses

NumberTitleHoursLecturers
263-2926-00 SDeep Learning for Big Code2 hrs
Mon16-18CAB G 52 »
V. Raychev

Groups

No information on groups available.

Restrictions

Places24 at the most
PriorityRegistration for the course unit is only possible for the primary target group
Primary target groupComputer Science MSc (263000)
Waiting listuntil 29.09.2018

Offered in

ProgrammeSectionType
Computer Science MasterSeminar in General StudiesWInformation
Computer Science MasterSeminar in Software EngineeringWInformation