Credits: 5

Schedule: 14.09.2016 - 09.12.2016

Teacher in charge (valid 01.08.2018-31.07.2020): 

Prof. Rohit Babbar (academic year 2018-2019), Prof. Juho Rousu (academic year 2019-2020).

Teaching Period (valid 01.08.2018-31.07.2020): 

III-IV (Spring)

Learning Outcomes (valid 01.08.2018-31.07.2020): 

After attending the course, the student knows how kernel methods can be used in various machine learning tasks, including classification, ranking and preference learning, as well as learning with multiple data sources and targets. The student knows how convex optimization methods can be used to efficiently train kernel-based models. The student knows how structured data such as sequences, hierarchies and graphs can be tackled through kernel methods.

Content (valid 01.08.2018-31.07.2020): 

Margin-based models and kernels. Classification and Support vector machines. Ranking and preference learning. Unsupervised learning with kernels. Kernels for structured data. Multilabel classification. Semi-supervised learning. Predicting structured output. Convex optimization methods.

Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

Exercises and exam.

Workload (valid 01.08.2018-31.07.2020): 

Lectures and exercises

Study Material (valid 01.08.2018-31.07.2020): 

Shawe-Taylor and Cristianini: Kernel Methods for Pattern Analysis, Cambridge University Press, 2004. Slides and research papers provided during the course.

Substitutes for Courses (valid 01.08.2018-31.07.2020): 

ICS-E4030 Kernel Methods in Machine Learning

Prerequisites (valid 01.08.2018-31.07.2020): 

Bachelor's degree in computer science and the course CS-E3210 / T-61.3050 Machine Learning: Basic principles (or equivalent knowledge).

Grading Scale (valid 01.08.2018-31.07.2020): 

0-5

Description