CS-E4830 - Kernel Methods in Machine Learning D, Lecture, 1.3.2023-8.6.2023

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Please note! Course description is confirmed for two academic years, 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: 01.03.2023 - 08.06.2023

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Rohit Babbar

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

  • valid for whole curriculum period:

    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 for whole curriculum period:

    Exercises and exam.

Workload

  • valid for whole curriculum period:

    Lectures and exercises

DETAILS

Substitutes for Courses
Prerequisites
SDG: Sustainable Development Goals

    4 Quality Education

    7 Affordable and Clean Energy

    9 Industry, Innovation and Infrastructure

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language : English

    Teaching Period : 2022-2023 Spring IV - V
    2023-2024 Spring IV - V

    Enrollment :

    Registration for Courses: In the academic year 2021-2022, registration for courses will take place on Sisu (sisu.aalto.fi) instead of WebOodi.

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