Machine learning the basics of machine learning, Slides of Machine Design

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Machine Learning: Lecture 1

Overview of Machine Learning

(Based on Chapter 1 of Mitchell T..,

Machine Learning , 1997)

Machine Learning: A Definition

Definition: A computer program is said to

learn from experience E with respect to some

class of tasks T and performance measure P, if

its performance at tasks in T, as measured by P,

improves with experience E.

Why is Machine Learning

Important?

Some tasks cannot be defined well, except by

examples (e.g., recognizing people).

 Relationships and correlations can be hidden within

large amounts of data. Machine Learning/Data

Mining may be able to find these relationships.

 Human designers often produce machines that do

not work as well as desired in the environments in

which they are used.

The amount of knowledge available about

certain tasks might be too large for explicit

encoding by humans (e.g., medical diagnostic).

 Environments change over time.

 New knowledge about tasks is constantly being

discovered by humans. It may be difficult to

continuously re-design systems “by hand”.

Why is Machine Learning

Important (Cont’d)?

Areas of Influence for Machine

Learning (Cont’d)

 Psychology: How to model human performance on

various learning tasks?

 Artificial Intelligence: How to write algorithms to

acquire the knowledge humans are able to acquire, at

least, as well as humans?

 Evolutionary Models: How to model certain aspects

of biological evolution to improve the performance

of computer programs?

Designing a Learning System:

An Example

1. Problem Description

2. Choosing the Training Experience

3. Choosing the Target Function

4. Choosing a Representation for the Target

Function

5. Choosing a Function Approximation Algorithm

6. Final Design

2. Choosing the Training Experience

 Direct versus Indirect Experience [Indirect Experience gives

rise to the credit assignment problem and is thus more difficult]

 Teacher versus Learner Controlled Experience [the teacher

might provide training examples; the learner might suggest interesting examples and ask the teacher for their outcome; or the learner can be completely on its own with no access to correct outcomes]

 How Representative is the Experience? [Is the training

experience representative of the task the system will actually have to solve? It is best if it is, but such a situation cannot systematically be achieved]

3. Choosing the Target Function

 (^) Given a set of legal moves, we want to learn how to choose the best move [since the best move is not necessarily known, this is an optimization problem]  (^) ChooseMove : B --> M is called a Target Function [ ChooseMove , however, is difficult to learn. An easier and related target function to learn is V : B --> R, which assigns a numerical score to each board. The better the board, the higher the score.]  (^) Operational versus Non-Operational Description of a Target Function [An operational description must be given]  (^) Function Approximation [The actual function can often not be learned and must be approximated]

5. Choosing a Function

Approximation Algorithm

 Generating Training Examples of the form <b,Vtrain(b)>

[e.g. <x1=3, x2=0, x3=1, x4=0, x5=0, x6=0, +100 (=blacks won)]  Useful and Easy Approach: Vtrain(b) <- V(Successor(b))

 Training the System

 Defining a criterion for success [What is the error that needs to be minimized?]  Choose an algorithm capable of finding weights of a linear function that minimize that error [e.g. the Least Mean Square (LMS) training rule].

^

6. Final Design for Checkers Learning

 (^) The Performance Module : Takes as input a new board and outputs a trace of the game it played against itself.  (^) The Critic : Takes as input the trace of a game and outputs a set of training examples of the target function  (^) The Generalizer : Takes as input training examples and outputs a hypothesis which estimates the target function. Good generalization to new cases is crucial.  (^) The Experiment Generator: Takes as input the current hypothesis (currently learned function) and outputs a new problem (an initial board state) for the performance system to explore In this course, we are mostly concerned with the generalizer