Introduction to Machine Learning.

A Brief Survey of Machine Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

ML Lectures Outline: what we will discuss? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

What is Learning? ,[object Object],[object Object],[object Object],definitions

Why Machine Learning? What ML can do? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Why Machine Learning? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Why Machine Learning? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

A General Model of Learning Agents

Three Aspects of Learning Systems ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

What are the aspects of research on Learning? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Some Issues in Machine Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Major Paradigms of Machine Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Major Paradigms of Machine Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

The Inductive Learning Problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

The Inductive Learning Problem ,[object Object],[object Object],[object Object],[object Object],[object Object]

Supervised Concept Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Inductive Learning Framework ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Inductive Learning by Nearest-Neighbor Classification ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Learning Decision Trees ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Color Shape Size + + - Size + - + big big small small round square red green blue

Preference Bias: Ockham's Razor ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Inductive Learning and Bias ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Example of using probabilities to create trees: Huffman code ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Huffman code example as a prototypical idea from other area ,[object Object],[object Object],[object Object],[object Object],[object Object],If we need to send many messages (A,B,C or D) and they have this probability distribution and we use this code , then over time, the average bits/message should approach 1.75 (= 0.125*3+0.125*3+0.25*2*0.5*1) .5 .5 1 .125 .125 .25 A C B D .25 0 1 0 0 1 1

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Gain ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],We will use this idea to build decision trees, ID3

Rule and Decision Tree Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Neural Network Learning ,[object Object],[object Object],[object Object]

Specifying A Learning Problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Example : Learning to Play Checkers ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

A Target Function for Learning to Play Checkers ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

A Training Procedure for Learning to Play Checkers ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Design Choices for Learning to Play Checkers Completed Design Determine Type of Training Experience Games against experts Games against self Table of correct moves Determine Target Function Board  value Board  move Determine Representation of Learned Function Polynomial Linear function of six features Artificial neural network Determine Learning Algorithm Gradient descent Linear programming

Example of Interesting Application: Data Mining Database Mining NCSA D2K - http://www.ncsa.uiuc.edu/STI/ALG

Example: Reasoning (Inference, Decision Support) Cartia ThemeScapes - http://www.cartia.com 6500 news stories from the WWW in 1997

Example: Planning and Control Normal Ignited Engulfed Destroyed Extinguished Fire Alarm Flooding DC-ARM - http://www-kbs.ai.uiuc.edu

Relevant Disciplines ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Machine Learning Symbolic Representation Planning/Problem Solving Knowledge-Guided Learning Bayes’s Theorem Missing Data Estimators PAC Formalism Mistake Bounds Language Learning Learning to Reason Optimization Learning Predictors Meta-Learning Entropy Measures MDL Approaches Optimal Codes ANN Models Modular Learning Occam’s Razor Inductive Generalization Power Law of Practice Heuristic Learning Bias/Variance Formalism Confidence Intervals Hypothesis Testing

Introduction to Machine Learning.

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Introduction to Machine Learning.