Generalization, Overfitting, and Model Selection

Generalization, Overfitting, and Model Selection

Generalization, Overfitting, and Model Selection Sample Complexity Results for Supervised Classification Maria-Florina (Nina) Balcan 10/03/2016 Two Core Aspects of Machine Learning Algorithm Design. How

Generalization and Overfitting

Generalization and Overfitting Model Selection Maria-Florina (Nina) Balcan February 24th, 2016 PAC/SLT models for Supervised Learning Data Source Distribution D on X Learning Algorithm Expert / Oracle

PAC Learning Introduction to Machine Learning. Matt Gormley Lecture 14 March 5, 2018

10-601 Introduction to Machine Learning Machine Learning Department School of Computer Science Carnegie Mellon University PAC Learning Matt Gormley Lecture 14 March 5, 2018 1 ML Big Picture Learning Paradigms:

Distributed Machine Learning. Maria-Florina Balcan, Georgia Tech

Distributed Machine Learning Maria-Florina Balcan, Georgia Tech Model for reasoning about key issues in supervised learning [Balcan-Blum-Fine-Mansour, COLT 12] Supervised Learning Example: which emails

Distributed Machine Learning. Maria-Florina Balcan Carnegie Mellon University

Distributed Machine Learning Maria-Florina Balcan Carnegie Mellon University Distributed Machine Learning Modern applications: massive amounts of data distributed across multiple locations. Distributed

Machine Learning

Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University October 11, 2012 Today: Computational Learning Theory Probably Approximately Coorrect (PAC) learning theorem

PAC-learning, VC Dimension and Margin-based Bounds

More details: General: http://www.learning-with-kernels.org/ Example of more complex bounds: http://www.research.ibm.com/people/t/tzhang/papers/jmlr02_cover.ps.gz PAC-learning, VC Dimension and Margin-based

Machine Learning. VC Dimension and Model Complexity. Eric Xing , Fall 2015

Machine Learning 10-701, Fall 2015 VC Dimension and Model Complexity Eric Xing Lecture 16, November 3, 2015 Reading: Chap. 7 T.M book, and outline material Eric Xing @ CMU, 2006-2015 1 Last time: PAC and

Computational Learning Theory. CS534 - Machine Learning

Computational Learning Theory CS534 Machine Learning Introduction Computational learning theory Provides a theoretical analysis of learning Shows when a learning algorithm can be expected to succeed Shows

Machine Learning

Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University October 11, 2012 Today: Computational Learning Theory Probably Approximately Coorrect (PAC) learning theorem

Computational Learning Theory

Computational Learning Theory Pardis Noorzad Department of Computer Engineering and IT Amirkabir University of Technology Ordibehesht 1390 Introduction For the analysis of data structures and algorithms

Understanding Generalization Error: Bounds and Decompositions

CIS 520: Machine Learning Spring 2018: Lecture 11 Understanding Generalization Error: Bounds and Decompositions Lecturer: Shivani Agarwal Disclaimer: These notes are designed to be a supplement to the

Sample and Computationally Efficient Active Learning. Maria-Florina Balcan Carnegie Mellon University

Sample and Computationally Efficient Active Learning Maria-Florina Balcan Carnegie Mellon University Machine Learning is Shaping the World Highly successful discipline with lots of applications. Computational

Machine Learning

Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 14, 2015 Today: The Big Picture Overfitting Review: probability Readings: Decision trees, overfiting

Foundations For Learning in the Age of Big Data. Maria-Florina Balcan

Foundations For Learning in the Age of Big Data Maria-Florina Balcan Modern Machine Learning New applications Explosion of data Classic Paradigm Insufficient Nowadays Modern applications: massive amounts

A Decision Stump. Decision Trees, cont. Boosting. Machine Learning 10701/15781 Carlos Guestrin Carnegie Mellon University. October 1 st, 2007

Decision Trees, cont. Boosting Machine Learning 10701/15781 Carlos Guestrin Carnegie Mellon University October 1 st, 2007 1 A Decision Stump 2 1 The final tree 3 Basic Decision Tree Building Summarized

Qualifying Exam in Machine Learning

Qualifying Exam in Machine Learning October 20, 2009 Instructions: Answer two out of the three questions in Part 1. In addition, answer two out of three questions in two additional parts (choose two parts

Introduction to Machine Learning CMU-10701

Introduction to Machine Learning CMU10701 11. Learning Theory Barnabás Póczos Learning Theory We have explored many ways of learning from data But How good is our classifier, really? How much data do we

The Perceptron algorithm

The Perceptron algorithm Tirgul 3 November 2016 Agnostic PAC Learnability A hypothesis class H is agnostic PAC learnable if there exists a function m H : 0,1 2 N and a learning algorithm with the following

CS340 Machine learning Lecture 5 Learning theory cont'd. Some slides are borrowed from Stuart Russell and Thorsten Joachims

CS340 Machine learning Lecture 5 Learning theory cont'd Some slides are borrowed from Stuart Russell and Thorsten Joachims Inductive learning Simplest form: learn a function from examples f is the target

Announcements - Homework

Announcements - Homework Homework 1 is graded, please collect at end of lecture Homework 2 due today Homework 3 out soon (watch email) Ques 1 midterm review HW1 score distribution 40 HW1 total score 35

PAC-learning, VC Dimension and Margin-based Bounds

More details: General: http://www.learning-with-kernels.org/ Example of more complex bounds: http://www.research.ibm.com/people/t/tzhang/papers/jmlr02_cover.ps.gz PAC-learning, VC Dimension and Margin-based

1 Active Learning Foundations of Machine Learning and Data Science. Lecturer: Maria-Florina Balcan Lecture 20 & 21: November 16 & 18, 2015

10-806 Foundations of Machine Learning and Data Science Lecturer: Maria-Florina Balcan Lecture 20 & 21: November 16 & 18, 2015 1 Active Learning Most classic machine learning methods and the formal learning

Lecture Slides for INTRODUCTION TO. Machine Learning. By: Postedited by: R.

Lecture Slides for INTRODUCTION TO Machine Learning By: alpaydin@boun.edu.tr http://www.cmpe.boun.edu.tr/~ethem/i2ml Postedited by: R. Basili Learning a Class from Examples Class C of a family car Prediction:

Learning Theory. Machine Learning CSE546 Carlos Guestrin University of Washington. November 25, Carlos Guestrin

Learning Theory Machine Learning CSE546 Carlos Guestrin University of Washington November 25, 2013 Carlos Guestrin 2005-2013 1 What now n We have explored many ways of learning from data n But How good

The Perceptron Algorithm, Margins

The Perceptron Algorithm, Margins MariaFlorina Balcan 08/29/2018 The Perceptron Algorithm Simple learning algorithm for supervised classification analyzed via geometric margins in the 50 s [Rosenblatt

Computational and Statistical Learning Theory

Computational and Statistical Learning Theory TTIC 31120 Prof. Nati Srebro Lecture 12: Weak Learnability and the l 1 margin Converse to Scale-Sensitive Learning Stability Convex-Lipschitz-Bounded Problems

COMS 4771 Introduction to Machine Learning. Nakul Verma

COMS 4771 Introduction to Machine Learning Nakul Verma Announcements HW2 due now! Project proposal due on tomorrow Midterm next lecture! HW3 posted Last time Linear Regression Parametric vs Nonparametric

Hypothesis Testing and Computational Learning Theory. EECS 349 Machine Learning With slides from Bryan Pardo, Tom Mitchell

Hypothesis Testing and Computational Learning Theory EECS 349 Machine Learning With slides from Bryan Pardo, Tom Mitchell Overview Hypothesis Testing: How do we know our learners are good? What does performance

Foundations For Learning in the Age of Big Data. Maria-Florina Balcan

Foundations For Learning in the Age of Big Data Maria-Florina Balcan Modern Machine Learning New applications Explosion of data Modern ML: New Learning Approaches Modern applications: massive amounts of

Introduction to Machine Learning Midterm Exam

10-701 Introduction to Machine Learning Midterm Exam Instructors: Eric Xing, Ziv Bar-Joseph 17 November, 2015 There are 11 questions, for a total of 100 points. This exam is open book, open notes, but

Introduction to Machine Learning. Introduction to ML - TAU 2016/7 1

Introduction to Machine Learning Introduction to ML - TAU 2016/7 1 Course Administration Lecturers: Amir Globerson (gamir@post.tau.ac.il) Yishay Mansour (Mansour@tau.ac.il) Teaching Assistance: Regev Schweiger

http://imgs.xkcd.com/comics/electoral_precedent.png Statistical Learning Theory CS4780/5780 Machine Learning Fall 2012 Thorsten Joachims Cornell University Reading: Mitchell Chapter 7 (not 7.4.4 and 7.5)

Computational Learning Theory: Probably Approximately Correct (PAC) Learning. Machine Learning. Spring The slides are mainly from Vivek Srikumar

Computational Learning Theory: Probably Approximately Correct (PAC) Learning Machine Learning Spring 2018 The slides are mainly from Vivek Srikumar 1 This lecture: Computational Learning Theory The Theory

Machine Learning. Computational Learning Theory. Le Song. CSE6740/CS7641/ISYE6740, Fall 2012

Machine Learning CSE6740/CS7641/ISYE6740, Fall 2012 Computational Learning Theory Le Song Lecture 11, September 20, 2012 Based on Slides from Eric Xing, CMU Reading: Chap. 7 T.M book 1 Complexity of Learning

COS 402 Machine Learning and Artificial Intelligence Fall Lecture 3: Learning Theory

COS 402 Machine Learning and Artificial Intelligence Fall 2016 Lecture 3: Learning Theory Sanjeev Arora Elad Hazan Admin Exercise 1 due next Tue, in class Enrolment Recap We have seen: AI by introspection

Active Learning and Optimized Information Gathering

Active Learning and Optimized Information Gathering Lecture 7 Learning Theory CS 101.2 Andreas Krause Announcements Project proposal: Due tomorrow 1/27 Homework 1: Due Thursday 1/29 Any time is ok. Office

Computational and Statistical Learning Theory

Computational and Statistical Learning Theory TTIC 31120 Prof. Nati Srebro Lecture 4: MDL and PAC-Bayes Uniform vs Non-Uniform Bias No Free Lunch: we need some inductive bias Limiting attention to hypothesis

VC dimension and Model Selection

VC dimension and Model Selection Overview PAC model: review VC dimension: Definition Examples Sample: Lower bound Upper bound!!! Model Selection Introduction to Machine Learning 2 PAC model: Setting A

The sample complexity of agnostic learning with deterministic labels

The sample complexity of agnostic learning with deterministic labels Shai Ben-David Cheriton School of Computer Science University of Waterloo Waterloo, ON, N2L 3G CANADA shai@uwaterloo.ca Ruth Urner College

Computational Learning Theory: Shattering and VC Dimensions. Machine Learning. Spring The slides are mainly from Vivek Srikumar

Computational Learning Theory: Shattering and VC Dimensions Machine Learning Spring 2018 The slides are mainly from Vivek Srikumar 1 This lecture: Computational Learning Theory The Theory of Generalization

Generalization theory

Generalization theory Chapter 4 T.P. Runarsson (tpr@hi.is) and S. Sigurdsson (sven@hi.is) Introduction Suppose you are given the empirical observations, (x 1, y 1 ),..., (x l, y l ) (X Y) l. Consider the

Does Unlabeled Data Help?

Does Unlabeled Data Help? Worst-case Analysis of the Sample Complexity of Semi-supervised Learning. Ben-David, Lu and Pal; COLT, 2008. Presentation by Ashish Rastogi Courant Machine Learning Seminar. Outline

Decision Tree Learning Lecture 2

Machine Learning Coms-4771 Decision Tree Learning Lecture 2 January 28, 2008 Two Types of Supervised Learning Problems (recap) Feature (input) space X, label (output) space Y. Unknown distribution D over

Support Vector Machines (SVMs).

Support Vector Machines (SVMs). SemiSupervised Learning. SemiSupervised SVMs. MariaFlorina Balcan 3/25/215 Support Vector Machines (SVMs). One of the most theoretically well motivated and practically most

Name (NetID): (1 Point)

CS446: Machine Learning (D) Spring 2017 March 16 th, 2017 This is a closed book exam. Everything you need in order to solve the problems is supplied in the body of this exam. This exam booklet contains

Machine Learning Theory (CS 6783)

Machine Learning Theory (CS 6783) Tu-Th 1:25 to 2:40 PM Kimball, B-11 Instructor : Karthik Sridharan ABOUT THE COURSE No exams! 5 assignments that count towards your grades (55%) One term project (40%)

VC dimension, Model Selection and Performance Assessment for SVM and Other Machine Learning Algorithms

03/Feb/2010 VC dimension, Model Selection and Performance Assessment for SVM and Other Machine Learning Algorithms Presented by Andriy Temko Department of Electrical and Electronic Engineering Page 2 of

References for online kernel methods

References for online kernel methods W. Liu, J. Principe, S. Haykin Kernel Adaptive Filtering: A Comprehensive Introduction. Wiley, 2010. W. Liu, P. Pokharel, J. Principe. The kernel least mean square

ECE 5424: Introduction to Machine Learning

ECE 5424: Introduction to Machine Learning Topics: Ensemble Methods: Bagging, Boosting PAC Learning Readings: Murphy 16.4;; Hastie 16 Stefan Lee Virginia Tech Fighting the bias-variance tradeoff Simple

Classification: The PAC Learning Framework

Classification: The PAC Learning Framework Machine Learning: Jordan Boyd-Graber University of Colorado Boulder LECTURE 5 Slides adapted from Eli Upfal Machine Learning: Jordan Boyd-Graber Boulder Classification:

VC Dimension Review. The purpose of this document is to review VC dimension and PAC learning for infinite hypothesis spaces.

VC Dimension Review The purpose of this document is to review VC dimension and PAC learning for infinite hypothesis spaces. Previously, in discussing PAC learning, we were trying to answer questions about

Machine Learning. Model Selection and Validation. Fabio Vandin November 7, 2017

Machine Learning Model Selection and Validation Fabio Vandin November 7, 2017 1 Model Selection When we have to solve a machine learning task: there are different algorithms/classes algorithms have parameters

Empirical Risk Minimization, Model Selection, and Model Assessment

Empirical Risk Minimization, Model Selection, and Model Assessment CS6780 Advanced Machine Learning Spring 2015 Thorsten Joachims Cornell University Reading: Murphy 5.7-5.7.2.4, 6.5-6.5.3.1 Dietterich,

Introduction to Machine Learning

Introduction to Machine Learning Vapnik Chervonenkis Theory Barnabás Póczos Empirical Risk and True Risk 2 Empirical Risk Shorthand: True risk of f (deterministic): Bayes risk: Let us use the empirical

Introduction to Machine Learning

Introduction to Machine Learning Slides adapted from Eli Upfal Machine Learning: Jordan Boyd-Graber University of Maryland FEATURE ENGINEERING Machine Learning: Jordan Boyd-Graber UMD Introduction to Machine

Learning Theory Continued

Learning Theory Continued Machine Learning CSE446 Carlos Guestrin University of Washington May 13, 2013 1 A simple setting n Classification N data points Finite number of possible hypothesis (e.g., dec.

Introduction to Machine Learning Midterm Exam Solutions

10-701 Introduction to Machine Learning Midterm Exam Solutions Instructors: Eric Xing, Ziv Bar-Joseph 17 November, 2015 There are 11 questions, for a total of 100 points. This exam is open book, open notes,

Day 3: Classification, logistic regression

Day 3: Classification, logistic regression Introduction to Machine Learning Summer School June 18, 2018 - June 29, 2018, Chicago Instructor: Suriya Gunasekar, TTI Chicago 20 June 2018 Topics so far Supervised

The Boosting Approach to. Machine Learning. Maria-Florina Balcan 10/31/2016

The Boosting Approach to Machine Learning Maria-Florina Balcan 10/31/2016 Boosting General method for improving the accuracy of any given learning algorithm. Works by creating a series of challenge datasets

12.1 A Polynomial Bound on the Sample Size m for PAC Learning

67577 Intro. to Machine Learning Fall semester, 2008/9 Lecture 12: PAC III Lecturer: Amnon Shashua Scribe: Amnon Shashua 1 In this lecture will use the measure of VC dimension, which is a combinatorial

Midterm Exam, Spring 2005

10-701 Midterm Exam, Spring 2005 1. Write your name and your email address below. Name: Email address: 2. There should be 15 numbered pages in this exam (including this cover sheet). 3. Write your name

Machine Learning. Computational Learning Theory. Eric Xing , Fall Lecture 9, October 5, 2016

Machine Learning 10-701, Fall 2016 Computational Learning Theory Eric Xing Lecture 9, October 5, 2016 Reading: Chap. 7 T.M book Eric Xing @ CMU, 2006-2016 1 Generalizability of Learning In machine learning

Machine Learning Theory (CS 6783)

Machine Learning Theory (CS 6783) Tu-Th 1:25 to 2:40 PM Hollister, 306 Instructor : Karthik Sridharan ABOUT THE COURSE No exams! 5 assignments that count towards your grades (55%) One term project (40%)

Lecture Learning infinite hypothesis class via VC-dimension and Rademacher complexity;

CSCI699: Topics in Learning and Game Theory Lecture 2 Lecturer: Ilias Diakonikolas Scribes: Li Han Today we will cover the following 2 topics: 1. Learning infinite hypothesis class via VC-dimension and

PAC Model and Generalization Bounds

PAC Model and Generalization Bounds Overview Probably Approximately Correct (PAC) model Basic generalization bounds finite hypothesis class infinite hypothesis class Simple case More next week 2 Motivating

Perceptron (Theory) + Linear Regression

10601 Introduction to Machine Learning Machine Learning Department School of Computer Science Carnegie Mellon University Perceptron (Theory) Linear Regression Matt Gormley Lecture 6 Feb. 5, 2018 1 Q&A

Machine Learning. Lecture 9: Learning Theory. Feng Li.

Machine Learning Lecture 9: Learning Theory Feng Li fli@sdu.edu.cn https://funglee.github.io School of Computer Science and Technology Shandong University Fall 2018 Why Learning Theory How can we tell

Lecture 3: Introduction to Complexity Regularization

ECE90 Spring 2007 Statistical Learning Theory Instructor: R. Nowak Lecture 3: Introduction to Complexity Regularization We ended the previous lecture with a brief discussion of overfitting. Recall that,

COMP9444: Neural Networks. Vapnik Chervonenkis Dimension, PAC Learning and Structural Risk Minimization

: Neural Networks Vapnik Chervonenkis Dimension, PAC Learning and Structural Risk Minimization 11s2 VC-dimension and PAC-learning 1 How good a classifier does a learner produce? Training error is the precentage

Advanced Introduction to Machine Learning CMU-10715

Advanced Introduction to Machine Learning CMU-10715 Risk Minimization Barnabás Póczos What have we seen so far? Several classification & regression algorithms seem to work fine on training datasets: Linear

Computational and Statistical Learning Theory

Computational and Statistical Learning Theory TTIC 31120 Prof. Nati Srebro Lecture 8: Boosting (and Compression Schemes) Boosting the Error If we have an efficient learning algorithm that for any distribution

Computational Learning Theory. CS 486/686: Introduction to Artificial Intelligence Fall 2013

Computational Learning Theory CS 486/686: Introduction to Artificial Intelligence Fall 2013 1 Overview Introduction to Computational Learning Theory PAC Learning Theory Thanks to T Mitchell 2 Introduction

Bayesian Learning (II)

Universität Potsdam Institut für Informatik Lehrstuhl Maschinelles Lernen Bayesian Learning (II) Niels Landwehr Overview Probabilities, expected values, variance Basic concepts of Bayesian learning MAP

ECE 5984: Introduction to Machine Learning

ECE 5984: Introduction to Machine Learning Topics: Ensemble Methods: Bagging, Boosting Readings: Murphy 16.4; Hastie 16 Dhruv Batra Virginia Tech Administrativia HW3 Due: April 14, 11:55pm You will implement

UNIVERSITY of PENNSYLVANIA CIS 520: Machine Learning Final, Fall 2013

UNIVERSITY of PENNSYLVANIA CIS 520: Machine Learning Final, Fall 2013 Exam policy: This exam allows two one-page, two-sided cheat sheets; No other materials. Time: 2 hours. Be sure to write your name and

Midterm, Fall 2003

5-78 Midterm, Fall 2003 YOUR ANDREW USERID IN CAPITAL LETTERS: YOUR NAME: There are 9 questions. The ninth may be more time-consuming and is worth only three points, so do not attempt 9 unless you are

Midterm exam CS 189/289, Fall 2015

Midterm exam CS 189/289, Fall 2015 You have 80 minutes for the exam. Total 100 points: 1. True/False: 36 points (18 questions, 2 points each). 2. Multiple-choice questions: 24 points (8 questions, 3 points

Gaussians Linear Regression Bias-Variance Tradeoff

Readings listed in class website Gaussians Linear Regression Bias-Variance Tradeoff Machine Learning 10701/15781 Carlos Guestrin Carnegie Mellon University January 22 nd, 2007 Maximum Likelihood Estimation

CS 6375: Machine Learning Computational Learning Theory

CS 6375: Machine Learning Computational Learning Theory Vibhav Gogate The University of Texas at Dallas Many slides borrowed from Ray Mooney 1 Learning Theory Theoretical characterizations of Difficulty

Machine Learning: Homework 5

0-60 Machine Learning: Homework 5 Due 5:0 p.m. Thursday, March, 06 TAs: Travis Dick and Han Zhao Instructions Late homework policy: Homework is worth full credit if submitted before the due date, half

Dan Roth 461C, 3401 Walnut

CIS 519/419 Applied Machine Learning www.seas.upenn.edu/~cis519 Dan Roth danroth@seas.upenn.edu http://www.cis.upenn.edu/~danroth/ 461C, 3401 Walnut Slides were created by Dan Roth (for CIS519/419 at Penn

CPSC 340: Machine Learning and Data Mining

CPSC 340: Machine Learning and Data Mining Linear Classifiers: predictions Original version of these slides by Mark Schmidt, with modifications by Mike Gelbart. 1 Admin Assignment 4: Due Friday of next

Machine Learning, Midterm Exam: Spring 2008 SOLUTIONS. Q Topic Max. Score Score. 1 Short answer questions 20.

10-601 Machine Learning, Midterm Exam: Spring 2008 Please put your name on this cover sheet If you need more room to work out your answer to a question, use the back of the page and clearly mark on the

Statistical Machine Learning from Data

Samy Bengio Statistical Machine Learning from Data 1 Statistical Machine Learning from Data Ensembles Samy Bengio IDIAP Research Institute, Martigny, Switzerland, and Ecole Polytechnique Fédérale de Lausanne

Holdout and Cross-Validation Methods Overfitting Avoidance

Holdout and Cross-Validation Methods Overfitting Avoidance Decision Trees Reduce error pruning Cost-complexity pruning Neural Networks Early stopping Adjusting Regularizers via Cross-Validation Nearest

Introduction to Machine Learning (67577) Lecture 5

Introduction to Machine Learning (67577) Lecture 5 Shai Shalev-Shwartz School of CS and Engineering, The Hebrew University of Jerusalem Nonuniform learning, MDL, SRM, Decision Trees, Nearest Neighbor Shai

Computational and Statistical Learning Theory

Computational and Statistical Learning Theory Problem set 1 Due: Monday, October 10th Please send your solutions to learning-submissions@ttic.edu Notation: Input space: X Label space: Y = {±1} Sample:

Regression and Classification" with Linear Models" CMPSCI 383 Nov 15, 2011!

Regression and Classification" with Linear Models" CMPSCI 383 Nov 15, 2011! 1 Todayʼs topics" Learning from Examples: brief review! Univariate Linear Regression! Batch gradient descent! Stochastic gradient

12 Statistical Justifications; the Bias-Variance Decomposition

Statistical Justifications; the Bias-Variance Decomposition 65 12 Statistical Justifications; the Bias-Variance Decomposition STATISTICAL JUSTIFICATIONS FOR REGRESSION [So far, I ve talked about regression

Computational Learning Theory

1 Computational Learning Theory 2 Computational learning theory Introduction Is it possible to identify classes of learning problems that are inherently easy or difficult? Can we characterize the number

Machine Learning Basics Lecture 4: SVM I. Princeton University COS 495 Instructor: Yingyu Liang

Machine Learning Basics Lecture 4: SVM I Princeton University COS 495 Instructor: Yingyu Liang Review: machine learning basics Math formulation Given training data x i, y i : 1 i n i.i.d. from distribution

CS229 Supplemental Lecture notes

CS229 Supplemental Lecture notes John Duchi 1 Boosting We have seen so far how to solve classification (and other) problems when we have a data representation already chosen. We now talk about a procedure,

Methods and Criteria for Model Selection. CS57300 Data Mining Fall Instructor: Bruno Ribeiro

Methods and Criteria for Model Selection CS57300 Data Mining Fall 2016 Instructor: Bruno Ribeiro Goal } Introduce classifier evaluation criteria } Introduce Bias x Variance duality } Model Assessment }

Learning Theory. Piyush Rai. CS5350/6350: Machine Learning. September 27, (CS5350/6350) Learning Theory September 27, / 14

Learning Theory Piyush Rai CS5350/6350: Machine Learning September 27, 2011 (CS5350/6350) Learning Theory September 27, 2011 1 / 14 Why Learning Theory? We want to have theoretical guarantees about our

An Introduction to Statistical Theory of Learning. Nakul Verma Janelia, HHMI

An Introduction to Statistical Theory of Learning Nakul Verma Janelia, HHMI Towards formalizing learning What does it mean to learn a concept? Gain knowledge or experience of the concept. The basic process

Lecture 25 of 42. PAC Learning, VC Dimension, and Mistake Bounds

Lecture 25 of 42 PAC Learning, VC Dimension, and Mistake Bounds Thursday, 15 March 2007 William H. Hsu, KSU http://www.kddresearch.org/courses/spring2007/cis732 Readings: Sections 7.4.17.4.3, 7.5.17.5.3,

Machine Learning (CS 567) Lecture 2

Machine Learning (CS 567) Lecture 2 Time: T-Th 5:00pm - 6:20pm Location: GFS118 Instructor: Sofus A. Macskassy (macskass@usc.edu) Office: SAL 216 Office hours: by appointment Teaching assistant: Cheol

FINAL: CS 6375 (Machine Learning) Fall 2014

FINAL: CS 6375 (Machine Learning) Fall 2014 The exam is closed book. You are allowed a one-page cheat sheet. Answer the questions in the spaces provided on the question sheets. If you run out of room for

An Algorithms-based Intro to Machine Learning

CMU 15451 lecture 12/08/11 An Algorithmsbased Intro to Machine Learning Plan for today Machine Learning intro: models and basic issues An interesting algorithm for combining expert advice Avrim Blum [Based