In this course, we will learn
- What machine learning for SE is
- What you can do with it
- How to do it
We will also learn
- How to read papers critically (this lecture!)
- How to discuss papers
- How to write SE papers
- How to do ML4SE in practice
- Malileh Izadi
- Amir Mir
- Elvan Kula
- Jeanderson Barros Cândido
How will we study
The course is a seminar / project based. Therefore:
- We will study papers, lots of them!
- We will write nice summaries
- We will present the results of our study in the classroom
- We will work in groups (5-6 people)
- We will develop ML methods for SE problems
How to get a paper
Q How much does one article cost?
Course deliverable: ML solution 4 SE problem
To finish this course:
- Select one of those research topics
- Use ML (however advanced or naive) to solve it. No heuristics
- Submit your paper + code + blog post
- Present your solution in front of the group
- 80% paper
- 10% final presentation
- 10% blog post
- P/F peer review
- Selection of topic: 4th Sep (this upcoming Friday)
- Students with no group or topic will not be allowed to take the
- First version of pipeline: 28th Sep
- Report + solution: 21rd Oct
- Peer-review: 26th Oct
- Final paper + blog post: 29th Oct
- Presentation: 30th Oct
TODO for today
- Form a group. Enrol yourself in a group in Brightspace.
- Explore the project ideas and select a topic.
- Post your project idea in the “Project proposals” forum in
- Join our Mattermost
Freely available on the web, on Georgios’s homepage (http://gousios.gr/courses/ml4se/)
You can print/download them before the lecture and bring them
along to make additional notes.
We are looking forward to improve them! If you have suggestions,
find mistakes etc, let us know!