Statistical Learning Theory
The mathematical theory of generalisation — VC dimension, PAC learning, and modern extensions.
§ 1Calendar
- Quiz 1—
- Quiz 2—
- End-term examination—
§ 2Previous Year Questions
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§ 3Resources
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Syllabus
The official course syllabus, transcribed from the IITM BS Data Science & Applications course booklet.
§ 1Course Description
This course introduces statistical learning theory, including models such as the PAC framework. It covers classical concepts like Vapnik's uniform convergence and modern ideas such as algorithmic stability, along with an introduction to unsupervised learning theory and a high-level overview of modern deep learning theory.
Lectures-Tutorials-Practicals-Credits: 4-0-0-4
§ 2Faculty
This course has not yet been introduced. Faculty and a week-by-week syllabus are not yet available — check the official handbook for the latest status.
§ 3Week-by-week
Week-by-week breakdown not yet available for this course.