Machine LearningLecture # 1

ContentsWhy machine learning (ML) useful ?

What is ML ?

Key steps of learning

Types of ML algorithms

Why Machine learning Computational power is available (Resource)Recent progress in algorithms and theory

(Resource)Growing flood of online data (Requirement)

Three niches of ML Data Mining: using historical data to improve

decisions, e.g. Medical record – medical knowledgeSoftware applications we can’t program by hand, e.g.

Speech recognition, handwritten recognition, autonomous driving

Self customizing programs, e.g. Amazon or Newsreaders that learn user interest

Typical Data Mining Task

Typical Data Mining Task

Typical Data Mining Task

Credit Risk Analysis

Speech recognitionFace recognitionRobotics control

Problems Too Difficult to Program by Hand

Problems Too Difficult to Program by HandIt is very hard to write programs that solve problems

like recognizing a face.We don’t know what program to write because we

don’t know how our brain does it.Even if we had a good idea about how to do it, the

program might be horrendously complicated.Instead of writing a program by hand, we collect lots

of examples that specify the correct output for a given input.

A machine learning algorithm then takes these examples and produces a program that does the job.The program produced by the learning algorithm

may look very different from a typical hand-written program. It may contain millions of numbers.

If we do it right, the program works for new cases as well as the ones we trained it on.

Problems Too Difficult to Program by Hand: Classic Example

What makes a 2?

Software that Customizes to Userwww. Amazon.com

www. Netflix.com

What is ML ? (1/2)Field of study that gives computer the

ability to learn without being explicitly programmed (Arthur Samuel, 1956)

Study of algorithms that improve their performance P at some task T with experience E (Tom Mitchell, 1998)

Well defined learning task: <P, T, E>

T: Play checkersP: % of games wonE: Playing against self

What is ML ? (2/2)Handwriting Recognition

Task T: recognizing and classifying handwritten words within images

Performance P: percent of words correctly classifiedTraining experience E: a database of written words

with given classificationML course grade prediction

Task T: predicting student grades for ML course Performance P: percent of grades correctly predictedTraining experience E: previous courses read by the

students and corresponding grades

Learning: Key Steps (1/4)Data: what past experience can we rely on ?

Names and grades of students in the past ML courses

Academic record of past and current studentsStudent name

Course title: ML

Course title: X

Course title: Y

Peter A B ADavid B A AJack ? C AKate ? A A

Training data

Current data

Learning: Key Steps (2/4)Assumption: to simplify the learning problem

The course has remained roughly the same over the years

Each student perform independently from othersRepresentation

Academic records are rather diverse so we might limit the summaries to select few courses. For example, we summaries the ith student (say peter) with vector

Xi=[A C B]Where grade may correspond to numeric values

Learning: Key Steps (3/4)Estimation

Given the training data: we need to find a mapping from “input vectors” x to “labels” y encoding the grades for the ML course.

Possible solution (nearest neighbor classifier)1. For any student x find the “closest” student xi in the training

set . Predict yi, the grade of the closest student 2. Evaluation: how can we tell how well our system is predicted?• We can wait till the end of this course• We can try to assess the accuracy based on the available data

Possible solution1. Divide the training set into training and test subsets 2. Training the classifier based on training subset and evaluate

it based on test subset

Learning: Key Steps (4/4)Model selection

Refinement To choose another classifier (instead of nearest

neighbor) To choose different representation (e.g. base the

summaries on different set of courses) Reducing assumptions (e.g. perhaps students work in

groups, etc)Analysing the performance:

We have to rely on the method of evaluating the accuracy of our predictions to select among the possible refinements

Types of ML AlgorithmsMajor main types are:

Supervised learningUnsupervised learningReinforcement learning Semi Supervised learning

Supervised Learning• A process of finding a model that

describes and distinguish data classes or concepts for the purpose of being able to predict the class of objects whose class label is unknown.

• Given a collection of records (training set )– Each record contains a set of attributes, one of the

attributes is the class.• Goal: previously unseen records should be

assigned a class as accurately as possible.– A test set is used to determine the accuracy of the

model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.

Supervised learning

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6 No Married 60K No

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8 No Single 85K Yes

9 No Married 75K No

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TestSet

Training Set Model

Learn Classifier

Attributes of input data

Class variable or output

Supervised Learning: Application• Direct Marketing– Goal: Reduce cost of mailing by targeting a set of

consumers likely to buy a new cell-phone product.– Approach:

• Use the data for a similar product introduced before. • We know which customers decided to buy and which

decided otherwise. This {buy, don’t buy} decision forms the class attribute.

• Collect various demographic, lifestyle, and company-interaction related information about all such customers.– Type of business, where they stay, how much they earn, etc.

• Use this information as input attributes to learn a classifier model.

Regression• Predict a value of a given continuous

valued variable based on the values of other variables, assuming a linear or nonlinear model of dependency.

• Greatly studied in statistics, neural network fields.

• Examples:– Predicting sales amounts of new product

based on advetising expenditure.– Predicting wind velocities as a function

of temperature, humidity, air pressure, etc.

– Time series prediction of stock market indices.

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Unsupervised Learning•Unlike supervised learning which analyse

class-labeled data objects, clustering analyse data objects without consulting a class. In fact class labels are not present in data because they are not known

• Major questions of the clustering are -Are there any “groups” in the data ?-What is each group ?-How many ?-How to identify them?

Clustering Definition• Given a set of data points, each having

a set of attributes, and a similarity measure among them, find clusters such that– Data points in one cluster are more

similar to one another.– Data points in separate clusters are less

similar to one another.• Similarity Measures:– Euclidean Distance if attributes are

continuous.–Other Problem-specific Measures.

Illustrating ClusteringEuclidean Distance Based Clustering in 3-D space

Intracluster distancesare minimized

Intercluster distancesare maximized

Clustering: Application• Market Segmentation:– Goal: subdivide a market into distinct subsets

of customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix.

– Approach: • Collect different attributes of customers

based on their geographical and lifestyle related information.• Find clusters of similar customers.• Measure the clustering quality by observing

buying patterns of customers in same cluster vs. those from different clusters.

Types of ML AlgorithmsReinforcement learning

– Supervised learning: Correct output for each training input is available

– Reinforcement learning: Some evaluation of an input is available, but not

the exact output

Reference LiteratureText book: Machine Learning by Tom Mitchell Reference book: Pattern recognition and

machine learning by C. Bishop