Foundations of Machine Learning
Lecture 8
Mehryar Mohri
Courant Institute and Google Research
mohri@cims.nyu.edu
Regression
Mehryar Mohri - Foundations of Machine Learning
page 2
Regression Problem
Training data: sample drawn i.i.d. from set X
according t

Foundations of Machine Learning
Lecture 6
Mehryar Mohri
Courant Institute and Google Research
mohri@cims.nyu.edu
Boosting
Mehryar Mohri - Foundations of Machine Learning
page 2
Weak Learning
(Kearns and Valiant, 1994)
Denition: concept class C is weakly P

Foundations of Machine Learning
Lecture 5
Mehryar Mohri
Courant Institute and Google Research
mohri@cims.nyu.edu
Kernel Methods
Motivation
Non-linear decision boundary.
Efcient computation of inner products in high
dimension.
Flexible selection of more co

Foundations of Machine Learning
Lecture 3
Mehryar Mohri
Courant Institute and Google Research
mohri@cims.nyu.edu
Learning Bounds for
Innite Hypothesis Sets
Motivation
With an innite hypothesis set H, the error bounds
of the previous lecture are not info

Foundations of Machine Learning
Lecture 4
Mehryar Mohri
Courant Institute and Google Research
mohri@cims.nyu.edu
Support Vector Machines
Mehryar Mohri - Foudations of Machine Learning
Binary Classication Problem
Training data: sample drawn i.i.d. from s

Foundations of Machine Learning
Lecture 2
Mehryar Mohri
Courant Institute and Google Research
mohri@cims.nyu.edu
PAC Model,
Guarantees for Learning with
Finite Hypothesis Sets
Motivation
Some computational learning questions
What can be learned efci

Foundations of Machine Learning
Lecture 1
Mehryar Mohri
Courant Institute and Google Research
mohri@cims.nyu.edu
Introduction to
Machine Learning
Logistics
Prerequisites: basics in linear algebra, probability,
and analysis of algorithms.
Workload: ab