Based on the book: "Understanding Machine Learning: From Theory to Algorithms" by Shai Shalev-Shwartz and Shai Ben-David
Course taught during the Artificial Intelligence master's program, 1st Year, 2nd Semester, 2021
University of Bucharest, Faculty of Mathematics and Computer Science
Professor: Bogdan Alexe
Laboratory: Bogdan Alexe
Exam: 50% * Assignment 1 + 50% * Assignment 2
- Empirical Risk Minimization
- Probably Approximately Correct learning
- Learning finite classes
- PAC learnability of a class H
- Agnostic PAC learning
- Uniform Convergence
- The No-Free-Lunch theorem
- The Bias-Complexity tradeoff
- Shattering and VC-dimension
- The fundamental theorem of statistical learning
- Lemma (Sauer – Shelah – Perles)
- AdaBoost Algorithm
AdaBoost | VC dimension and hypothesis finding algorithm for all strings of size M subspace |
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