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: 02.03.2022 - 02.06.2022
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
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
SDG: Sustainable Development Goals
7 Affordable and Clean Energy
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2022-2023 Spring IV - V
2023-2024 Spring IV - VEnrollment :
Registration for Courses: In the academic year 2021-2022, registration for courses will take place on Sisu (sisu.aalto.fi) instead of WebOodi.