Chapter 1 cont Introduction to Machine Learning 1

Review: Learning Definition • Well-posed learning problem: – Improve on task, T, with respect

Examples • • • T: Playing checkers P: Percentage of games won against an

Designing a learning system 1. Choosing the training experience (data set) 2. Choosing the

Choosing the Training Experience – Sometimes straightforward • Text classification, disease diagnosis – Sometimes

Training Experience Attributes • How the training experience is controlled by the learner? –

Choosing the Target Function • For checkers: – Could learn a function: Choose. Move(board,

Ideal definition of V(b) • If b is a final winning board, then V(b)

Linear Function for Representing V(b) bp(b): number of black pieces on board b rp(b):

A win board example • < <bp=3, rp=0, bk=1, rk=0, bt=0, rt=0>, 100> (win

Examples of Value Functions • Linear Regression – Input: feature vectors – Output: n

Examples of Classifiers • Linear Classifier – Input: feature vectors – Output:

Examples of Classifiers n Rule Classifier n Decision tree n n A tree with

Learning • Approximating the weights using the data set

Least Mean Square, Gradient Discent MSE (mean squared error):

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Chapter 1. cont. Introduction to Machine Learning 1

Review: Learning Definition • Well-posed learning problem: – Improve on task, T, with respect to performance metric, P, based on experience, E. • The main issue of Learning: – Finding a general function from specific training examples

Examples • • • T: Playing checkers P: Percentage of games won against an arbitrary opponent E: Playing practice games against itself • • • T: Recognizing hand-written words P: Percentage of words correctly classified E: Database of human-labeled images of handwritten words • • T: Driving on four-lane highways using vision sensors P: Average distance traveled before a human-judged error E: A sequence of images and steering commands recorded while observing a human driver. • • • T: Categorize email messages as spam or legitimate. P: Percentage of email messages correctly classified. E: Database of emails, some with human-given labels

Designing a learning system 1. Choosing the training experience (data set) 2. Choosing the target function 3. Choosing a representation for the target function 4. Choosing a function approximation algorithm 5. The final design

Choosing the Training Experience – Sometimes straightforward • Text classification, disease diagnosis – Sometimes not so straightforward • Chess playing, checkers (indirect information is available)

Training Experience Attributes • How the training experience is controlled by the learner? – Is it provided by a human process outside the learner’s control? – Does learner collect the training examples by autonomously exploring its environment? • How well it represent the distribution of the examples? – Playing checkers: Playing practice games against itself

Choosing the Target Function • For checkers: – Could learn a function: Choose. Move(board, legal-moves) → best-move – Or could learn an evaluation function, V(board) → R, Where R is a real value representing how favorable the board is.

Ideal definition of V(b) • If b is a final winning board, then V(b) = 100 • If b is a final losing board, then V(b) = – 100 • If b is a final draw board, then V(b) = 0 • Otherwise, then V(b) = V(b’), where b’ is the highest scoring final board position that is achieved starting from b and playing optimally until the end of the game (assuming the opponent plays optimally as well). This definition is non-operational => Approximation of the ideal function

Linear Function for Representing V(b) bp(b): number of black pieces on board b rp(b): number of red pieces on board b bk(b): number of black kings on board b rk(b): number of red kings on board b bt(b): number of black pieces threatened (i. e. which can be immediately taken by red on its next turn) – rt(b): number of red pieces threatened – – –

A win board example • < <bp=3, rp=0, bk=1, rk=0, bt=0, rt=0>, 100> (win for black) • Training Examples: {<b, V(b)>}

Examples of Value Functions • Linear Regression – Input: feature vectors – Output: n Logistic Regression n n Input: feature vectors Output:

Examples of Classifiers • Linear Classifier – Input: feature vectors – Output:

Examples of Classifiers n Rule Classifier n Decision tree n n A tree with nodes representing condition testing and leaves representing classes Decision list n If condition 1 then class 1 elseif condition 2 then class 2 elseif ….

Learning • Approximating the weights using the data set

Least Mean Square, Gradient Discent MSE (mean squared error):

Gradient Descent

Design