E0 270 Machine Learning
Lecture 3 (Jan 15, 2013)
Discriminative Probabilistic Models for Classification
Lecturer: Shivani Agarwal
Disclaimer: These notes are a brief summary of the topics covered in the lecture.
They are not a substitute for the full lect

E0 270 Machine Learning
Lecture 4 (Jan 17, 2013)
Least Squares Regression
Lecturer: Shivani Agarwal
Disclaimer: These notes are a brief summary of the topics covered in the lecture.
They are not a substitute for the full lecture.
Outline
Regression and c

E0 370 Statistical Learning Theory
Lecture 3 (Aug 16, 2011)
Uniform Convergence and Growth Function/VC-Entropy
Lecturer: Shivani Agarwal
1
Scribe: Shivani Agarwal
Introduction
In the previous lecture we reviewed the SVM learning algorithm, which when give

A Probabilistic Interpretation of
Canonical Correlation Analysis
Francis R. Bach
Computer Science Division
University of California
Berkeley, CA 94114, USA
fbach@cs.berkeley.edu
Michael I. Jordan
Computer Science Division
and Department of Statistics
Univ

E0 370 Statistical Learning Theory
Lecture 15 (Oct 20, 2011)
Boosting
Lecturer: Shivani Agarwal
1
Scribe: Anil C R
Introduction
We start by discussing some variants of the learnability model we saw in the last few lectures, and consider
whether efficient

E0 270 Machine Learning
Lecture 24 (Apr 4, 2013)
A Glimpse into Statistical Learning Theory: Statistical Consistency of
Binary Classification Algorithms Based on Risk Minimization
Lecturer: Shivani Agarwal
Disclaimer: These notes are a brief summary of th

E0 370 Statistical Learning Theory
Lecture 4 (Aug 23, 2011)
VC-Dimension and Sauers Lemma
Lecturer: Shivani Agarwal
1
Scribe: Achintya Kundu
Introduction
In the previous lecture we saw the technique of uniform convergence for obtaining confidence bounds o

E0 270 Machine Learning
Lecture 1 (Jan 8, 2013)
Introduction. Binary Classification and Bayes Error.
Lecturer: Shivani Agarwal
Disclaimer: These notes are a brief summary of the topics covered in the lecture.
They are not a substitute for the full lecture

E0 270 Machine Learning
Lecture 2 (Jan 10, 2013)
Generative Probabilistic Models for Classification
Lecturer: Shivani Agarwal
Disclaimer: These notes are a brief summary of the topics covered in the lecture.
They are not a substitute for the full lecture.

E0 270 Machine Learning
Lecture 5 (Jan 22, 2013)
Support Vector Machines for Classification and Regression
Lecturer: Shivani Agarwal
Disclaimer: These notes are a brief summary of the topics covered in the lecture.
They are not a substitute for the full l

E0 270 Machine Learning
Lecture 10 (Feb 7, 2013)
Canonical Correlation Analysis
Lecturer: Chiranjib Bhattacharyya
Scribe: Debarghya Ghoshdastidar
Disclaimer: These notes are a brief summary of the topics covered in the lecture.
They are not a substitute f