machine learning: machine learning: introduction introduction

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1 Machine Learning: Machine Learning: Introduction Introduction Dr Valentina Plekhanova University of Sunderland, UK Valentina Plekhanova Machine Learning: Introduction 2 The Field The Field of of Machine Learning Machine Learning The The goal of machine learning goal of machine learning is to develop methods, is to develop methods, techniques and tools for building techniques and tools for building intelligent learning intelligent learning machines machines , that can solve the problem in combination with , that can solve the problem in combination with an available data set of training examples an available data set of training examples . Note: A Note: A learning machine or an algorithm learning machine or an algorithm does not solve does not solve the problem directly. the problem directly. Valentina Plekhanova Machine Learning: Introduction 3 The Meaning of Learning The Meaning of Learning When a learning machine improves its performance at a given task over time, without reprogramming, it can be said to have learned something. Learning: improvement of performance with experience at a given task improve over task T with respect to performance measure P based on experience E Example from Machine/Computer Vision field: Example from Machine/Computer Vision field: learn to recognise objects from a visual scene or an image learn to recognise objects from a visual scene or an image T: identify all objects : identify all objects P: accuracy (e.g. a number of objects correctly recognised) : accuracy (e.g. a number of objects correctly recognised) E: a database of objects recorded : a database of objects recorded

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Page 1: Machine Learning: Machine Learning: Introduction Introduction

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Machine Learning:Machine Learning:IntroductionIntroduction

Dr Valentina PlekhanovaUniversity of Sunderland, UK

Valentina Plekhanova Machine Learning: Introduction 2

The Field The Field ofof Machine LearningMachine Learning

The The goal of machine learninggoal of machine learning is to develop methods, is to develop methods, techniques and tools for building techniques and tools for building intelligent learning intelligent learning machinesmachines, that can solve the problem in combination with , that can solve the problem in combination with an available data set of training examplesan available data set of training examples..

Note: A Note: A learning machine or an algorithmlearning machine or an algorithm does not solve does not solve the problem directly.the problem directly.

Valentina Plekhanova Machine Learning: Introduction 3

The Meaning of LearningThe Meaning of Learning

When a learning machine improves its performance at a given task over time, without reprogramming, it can be said to have learned something.

Learning: improvement of performance with experience at a given taskimprove over task Twith respect to performance measure Pbased on experience E

Example from Machine/Computer Vision field:Example from Machine/Computer Vision field:learn to recognise objects from a visual scene or an imagelearn to recognise objects from a visual scene or an image

TT: identify all objects: identify all objectsPP: accuracy (e.g. a number of objects correctly recognised): accuracy (e.g. a number of objects correctly recognised)EE: a database of objects recorded : a database of objects recorded

Page 2: Machine Learning: Machine Learning: Introduction Introduction

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Valentina Plekhanova Machine Learning: Introduction 4

Components Components of of aa Learning ProblemLearning Problem

Task: the behaviour or task that’s being improved, e.g.classification, object recognition, acting in an environment.

Data: the experiences that are being used to improve performance in the task.

Measure of improvements: How can the improvement be measured? Examples:

Provide more accurate solutions (e.g. increasing the accuracy inprediction)

Cover a wider range of problemsObtain answers more economically (e.g. improved speed)Simplify codified knowledge New skills that were not presented initially

Valentina Plekhanova Machine Learning: Introduction 5

Notes on Notes on Learning Problem & Learning TasksLearning Problem & Learning Tasks

Learning ProblemLearning Problem: Precise model that describes:

what is to be learned

how it is done

what measures are to be used in analysing and comparing the performance of different solutions.

Learning tasksLearning tasks are formal definitions of “are formal definitions of “what we want to what we want to learnlearn”. ”.

Valentina Plekhanova Machine Learning: Introduction 6

Learning AlgorithmLearning Algorithm

In Machine learning: Learning algorithm - Learning Agent.

Note: A learning agent is not concerned with constructing Note: A learning agent is not concerned with constructing an exact answer (e.g. an exact concept definition).an exact answer (e.g. an exact concept definition).

Page 3: Machine Learning: Machine Learning: Introduction Introduction

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Valentina Plekhanova Machine Learning: Introduction 7

Learning FeedbackLearning Feedback

[ Sen & Weiss, 1999 ]

Learning feedback can be provided by the system environment or the agents themselves.

Supervised learning: specifies the desired activities/objectives of learning – feedback from a teachera teacher

Unsupervised learning: no explicit feedback is provided and the objective is to find out useful and desired activities on the basis of trial-and-error and self-organisation processes – a passive observera passive observer

Reinforcement learning: specifies the utility of the actual activity of the learner and the objectives is to maximise this utility – feedback from a critic a critic

Valentina Plekhanova Machine Learning: Introduction 8

Ways Ways of of LearningLearning

Rote learning, i.e. learning from memory; in a mechanical way

Learning from examples and by practice

Learning from instructions/advice/explanations

Learning by analogy

Learning by discovery

Valentina Plekhanova Machine Learning: Introduction 9

Inductive Inductive && Deductive LearningDeductive Learning

Inductive LearningInductive Learning: Reasoning from a set of examples to produce a general rules. The rules should be applicable to new examples, but there is no guarantee that the result will be correct.

Deductive LearningDeductive Learning: Reasoning from a set of known facts and rules to produce additional rules that are guaranteed to be true.

Page 4: Machine Learning: Machine Learning: Introduction Introduction

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Valentina Plekhanova Machine Learning: Introduction 10

Assessment Assessment of of Learning AlgorithmsLearning Algorithms

The most common criteria for learning algorithms The most common criteria for learning algorithms assessments are:assessments are:

Accuracy Accuracy (e.g. percentages of correctly classified (e.g. percentages of correctly classified ++’s and ’s and ––’s)’s)EfficiencyEfficiency (e.g. examples needed, computational tractability)(e.g. examples needed, computational tractability)RobustnessRobustness (e.g. against noise, against incompleteness) (e.g. against noise, against incompleteness) Special requirementsSpecial requirements (e.g. (e.g. incrementalityincrementality, concept drift), concept drift)Concept complexityConcept complexity (e.g. representational issues (e.g. representational issues –– examples & BK)examples & BK)TransparencyTransparency (e.g. comprehensibility for the human user)(e.g. comprehensibility for the human user)

Valentina Plekhanova Machine Learning: Introduction 11

Some Theoretical SettingsSome Theoretical Settings

Inductive Logic Programming (ILP)Inductive Logic Programming (ILP)

Probably Approximately Correct (PAC) LearningProbably Approximately Correct (PAC) Learning

Learning as Optimisation (Tasks: Reinforcement Learning)Learning as Optimisation (Tasks: Reinforcement Learning)

Bayesian LearningBayesian Learning

……

Valentina Plekhanova Machine Learning: Introduction 12

““Each task can be related to one or more methods”Each task can be related to one or more methods”

Concept Learning: an ExampleConcept Learning: an Example

MethodsMethods: Lazy Learning; Incremental Decision Tree : Lazy Learning; Incremental Decision Tree Learning; Boosting; Support Vector Machines (SVM). Learning; Boosting; Support Vector Machines (SVM).

TheoriesTheories: e.g. Inductive Logic Programming (ILP); : e.g. Inductive Logic Programming (ILP); Probably Approximately Correct (PAC) Learning; Probably Approximately Correct (PAC) Learning; Bayesian Learning; Statistical Learning.Bayesian Learning; Statistical Learning.

Page 5: Machine Learning: Machine Learning: Introduction Introduction

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Valentina Plekhanova Machine Learning: Introduction 13

SummarySummaryA Model of Learning: Key AspectsA Model of Learning: Key Aspects

Learner: who or what is doing the learning, e.g. an algorithm, a computer program.

Domain: what is being learned, e.g. a function, a concept.

Goal: why the learning is done.

Representation: the way the objects to be learned are represented.

Algorithmic Technology: the algorithmic framework to be used, e.g. decision trees, lazy learning, artificial neural networks, support vector machines. [Sen & Weiss, 1999 ]

Valentina Plekhanova Machine Learning: Introduction 14

SummarySummaryA Model of Learning: Key AspectsA Model of Learning: Key Aspects

Information Source: the information/training data the program uses for learning, e.g. positive/negative examples, feedback from actions.

Training/Learning Scenario: the description of the learning process, e.g. interactive, supervised / unsupervised, etc.

Prior Knowledge: what is known in advance about the domain, e.g. about specific properties of the concepts to be learned.

Success Criteria: the criteria for successful learning.

Performance: e.g. the amount of time, space and computational power needed in order to learn a certain task.

[Sen & Weiss, 1999 ]