MSRI Workshop on Nonlinear Estimation and Classification, 2002.
The Boosting Approach to Machine Learning
An Overview
Robert E. Schapire
AT&T Labs ; Research
Shannon Laboratory
180 Park Avenue, Room A203
Florham Park, NJ 07932 USA
www.research.att.com/ sc
CS464
Introduction to
Machine Learning
Lecture 2
znur Tatan
Bilkent University
Administrivia
Announcements:
Reading material uploaded on Moodle
The syllabus is also on Moodle
You should start arranging your teams
Any questions on admin stuff?
Uncerta
CS464
Introduction to
Machine Learning
Lecture 3
znur Tatan
Bilkent University
Admistrivia
The homework 1 will be next week. It will be on
probability about 6 questions, there will be one
programming question.
Outline
Last time we have started on a revi
CS464
Introduction to
Machine Learning
Lecture 5
znur Tatan
Bilkent University
Administrivia
Midterm date is set.
Nov 26 Tuesday 18:00 20:00 pm
HW1 is due Oct 11 5:00 pm
Well talk about projects (deadlines and content) at
the end of the second hour
Yo
CS464
Introduction to
Machine Learning
Lecture 4
znur Tatan
Bilkent University
Administrivia
Office hours announced included in the syllabus
znur Tatan
Office: EA 429
Office Hour: By appointment
Teaching Assistant
Ahmet cen
e-mail: ahmet. iscen@bilke
CS464
Introduction to
Machine Learning
Lecture 6
znur Tatan
Bilkent University
Administrivia
Homework is due this Friday 5:00 pm sharp
(Moodle upload will be closed after that).
Detailed Project Information is on Moodle
Project Proposal Presentation, O
CS464
Introduction to
Machine Learning
Lecture 9
znur Tatan
Bilkent University
Administrivia
Next deadlines on the project:
- Progress Report Dec 2 2013, 5:00 pm
Progress Presentation Dec 4, 20 2013 in class
Decision Trees
Non-linear classifier
Easy to
CHAPTER 1
GENERATIVE AND DISCRIMINATIVE
CLASSIFIERS:
NAIVE BAYES AND LOGISTIC REGRESSION
Machine Learning
Copyright c 2005, 2010. Tom M. Mitchell. All rights reserved.
*DRAFT OF January 19, 2010*
*PLEASE DO NOT DISTRIBUTE WITHOUT AUTHORS
PERMISSION*
This
CS464
Introduction to
Machine Learning
Lecture 7
znur Tatan
Bilkent University
Administrivia
Project Proposal Presentation, Oct 23 Wed, in
class, 5% of the project grade
Upload your ppt, pptx or pdf by Oct 22 10 pm.
Prepare at most 4-to-5 slides.
Proj
CS464
Introduction to
Machine Learning
Lecture 10
znur Tatan
Bilkent University
Administrivia
Next deadlines on the project:
Progress Presentation Dec 2, 20 2013 in class
Progress Report Dec 4 2013, 5:00 pm
Decision Trees
Non-linear classifier
Easy to
CS464
Introduction to
Machine Learning
Lecture 8
znur Tatan
Bilkent University
Administrivia
Next deadlines on the project:
Progress Presentation Nov 18, 20 2013 in class
Progress Report Nov 21 2013, 5:00 pm
Outline
Estimating Test Error and Model Sel
CS464
Introduction to
Machine Learning
Lecture 11
znur Tatan
Bilkent University
Administrivia
Homework 2 is due Nov 19 Sunday 22:00 pm
Exam on Nov 26 Tuesday 6:00 pm
Everything covered in lectures (including slides and
the board + what we talked) + req
CS464 Introduction to Machine Learning
Fall 2009 Programming Assignment 2 Nave Bayes Classifier Due Date: December 11, 2009
In this programming assignment, you are going to write a text categorization program using Nave Bayes Classifier. In the given corp
CS464 Introduction to Machine Learning
Fall 2009 Programming Assignment 1 Decision Tree Learning Due Date: November 4, 2009
In this programming assignment, you are going to implement decision tree algorithm ID3. Your program is going to use three input fi
Genetic Algorithms
Genetic algorithms provide an approach to learning that is based loosely on simulated evolution. Hypotheses are often described by bit strings whose interpretation depends on the application. The search for an appropriate hypothesis be
CS464 Introduction to Machine Learning
Fall 2009 Homework 1 Concept Learning Due Date: October 14, 2009
Assume that the following training examples are given: Ex 1 2 3 4 Attrb1 a a b c Attrb2 b b c b Attrb3 b b a b Attrb4 b a a b Attrb5 a a b b TargetAttr
CS464 Introduction to Machine Learning
Fall 2009 Homework 2 Decision Tree Learning Due Date: October 23, 2009
Q1) Give decision trees to represent the following boolean functions: A B A [B C] A XOR B [A B] [C D] Q2) Consider the following set of training
CS464 Introduction to Machine Learning
Fall 2009 Homework 3 Nueral Networks Due Date: November 18, 2009 Q1) a) Design a two-input perceptron that implements the boolean function A B. b) Give the trace of the perceptron learning algorithm for this function
Concept Learning
Inducing general functions from specific training examples is a main issue of machine learning. Concept Learning: Acquiring the definition of a general category from given sample positive and negative training examples of the category. C
Lexical Analyzer
Lexical Analyzer reads the source program character by character to produce tokens. Normally a lexical analyzer doesnt return a list of tokens at one shot, it returns a token when the parser asks a token from it.
source program
Lexical A
Decision Tree Learning
Decision tree learning is a method for approximating discrete-valued target functions. The learned function is represented by a decision tree.
A learned decision tree can also be re-represented as a set of if-then rules.
Decision
Artificial Neural Networks
Artificial neural networks (ANNs) provide a general, practical method for learning real-valued, discrete-valued, and vector-valued functions from examples. Algorithms such as BACKPROPAGATION gradient descent to tune network par
Bayesian Learning
Features of Bayesian learning methods: Each observed training example can incrementally decrease or increase the estimated probability that a hypothesis is correct.
This provides a more flexible approach to learning than algorithms that