Machine Learning for Structured Models
|Dmitrij Schlesinger, Sommer semester 2017|
A common opinion is that the main task of Machine Learning is to establish a connection between raw data and their semantically meaningful interpretations. A bit more poetic: "to teach computers to think" (or at least to understand the data). Machine Learning approaches have numerous applications in many different subject areas, especially in those where data uncertainty plays a crucial role, like e.g. Natural Language Processing, Computer Vision etc. This lecture focuses on structured models, where the estimated quantity has a complex structure, i.e. it is not just a number or a class but e.g. a graph or a sequence or a grid, whatever.
Lectures: Friday, 3DS, 11:10-12:40, INF E023, Start: 07.04.2017
Exercises: Monday, 4DS, 13:00-14:30, INF E007 and Thursday, 2DS, 9:20-10:50, INF E007, Start: 24.04 and 27.04 respectively
Prerequisites: Solid mathematical background. This lecture is especially suitable for people, who attended Machine Learning I and like to deepen their knowlege. Of course, the lecture is open to everyone, but if you did not attend "Machine Learning I", be ready for self-study.
Extent: 2/2/0, Exam: Oral exam after the semester, Enrollment: jExam, if does not work – email, Maximum attendees: 60
Note: The lectures will be held in English.
|29.03.2017: Welcome! On this page, all required information is provided during the semester – lecture scripts, exercise assignments, some actual informations, whatever. The scripts will appear during the semester, step by step. See you at the first lecture on Friday 07.04.|