Please note! Course description is confirmed for two academic years (1.8.2018-31.7.2020), which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
LEARNING OUTCOMES
After attending the course, the student knows the basics of kernels, positive-definiteness, and RKHS. The courses also formally introduces the notions of generalization in machine learning by studying the principle of Empirical Risk Minimization and its consistency. The student knows how convex optimization methods can be used to efficiently train kernel-based and large-scale linear models.It is also discussed how to apply kernel based methods for unsupervised learning such as PCA
Credits: 5
Schedule: 03.03.2021 - 03.06.2021
Teacher in charge (valid 01.08.2020-31.07.2022): Rohit Babbar
Teacher in charge (applies in this implementation): Rohit Babbar
Contact information for the course (applies in this implementation):
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
Valid 01.08.2020-31.07.2022:
Inner product spaces, Kernels, Reproducing kernels, and RKHS. Introductory learning theory and Generalization. Empirical Risk Minimization, Uniform Convergence and Rademacher Complexity. Kernel Ridge Regression and Logistic Regression. Optimization and Duality. Margin-based methods and Support vector machines. Unsupervised learning including clustering, PCA and their kernel variants.
Assessment Methods and Criteria
Valid 01.08.2020-31.07.2022:
Exercises and exam.
Workload
Valid 01.08.2020-31.07.2022:
Lectures and exercises
DETAILS
Study Material
Valid 01.08.2020-31.07.2022:
Shawe-Taylor and Cristianini: Kernel Methods for Pattern Analysis, Cambridge University Press, 2004. Slides and research papers provided during the course.
Prerequisites
Valid 01.08.2020-31.07.2022:
Bachelor's degree in computer science and the course CS-C3240 Machine Learning or CS-E3210 Machine Learning: Basic principles (or equivalent knowledge).
SDG: Sustainable Development Goals
7 Affordable and Clean Energy
FURTHER INFORMATION
- Teacher: Babbar Rohit
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- Teacher: Kulkarni Tejas
- Teacher: Mikkola Petrus
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