CHAPTER 1: INTRODUCTION

Transcription

1 CHAPTER 1: INTRODUCTION

2 Big Data 3 Widespread use of personal computers and wireless communication leads to big data We are both producers and consumers of data Producer: when buy a product, rent a movie, write a blog, post on social media Consumer: we want to have products and services specialized to us, expect our needs to be understood and interests to be predicted Data is not random, it has structure, e.g., customer behavior We need big theory to extract that structure from data for (a) Understanding the process (b) Making predictions for the future

3 Why Learn? 4 Machine learning is programming computers to optimize a performance criterion using example data or past experience. There is no need to learn to calculate payroll Learning is used when: Human expertise does not exist (navigating on Mars), Humans are unable to explain their expertise (speech recognition) Solution changes in time (routing on a computer network) Solution needs to be adapted to particular cases (user biometrics) Seems like there is a hidden pattern in data but we can not pinpoint it

4 5 What We Talk About When We Talk About Learning? Learning general models from a data of particular examples Data is cheap and abundant (data warehouses, data marts); knowledge is expensive and scarce. Example in retail: Customer transactions to consumer behavior: People who bought Blink also bought Outliers ( Build a model that is a good and useful approximation to the data. Allows us to predict future behavior, find hidden patterns etc.

5 Data Mining 6 Applications of machine learning to large databases is called Data Mining In data mining a large volume of data is processed to construct a simple model with valuable use, e.g., having high predictive accuracy There are abundant such application areas Retail: Market basket analysis, Customer relationship management (CRM) Finance: Credit scoring, fraud detection Manufacturing: Control, robotics, troubleshooting Medicine: Medical diagnosis Telecommunications: Spam filters, intrusion detection Bioinformatics: Motifs, alignment Web mining: Search engines...

6 What is Machine Learning? 7 Machine learning is not just database problem, it is also a part of Artificial Intelligence (AI) To be intelligent it must have ability to learn Machine learning is programming computers to optimize a performance criterion using example data or past experience. This is often done by building a model (say, a predictive model based on past data for performing prediction on future data) Such models are described by a number of parameters Learning involves execution of a computer program to optimize the values of these parameters that in effect optimizes the performance criteria Thus machine learning is essentially solving an optimization problem efficiently using computers

7 What is Machine Learning? 8 Role of Statistics/Probability: Machine learning uses theory of statistics in building mathematical models to solve an optimization problem Such a model is used to make inference from a sample Role of Computer science: Two fold role First, we need efficient algorithms to solve the optimization problem (aka, training a model) as well as to store and process possibly massive amount of data Second, once a model is learned, its representation and algorithmic solution for inference needs to be efficient as well

8 Examples of machine learning Applications 9 Association Supervised Learning Classification Regression Unsupervised Learning Clustering Dimension reduction Reinforcement Learning

9 Learning Associations 10 Market Basket analysis: Learning conditional probability P (Y X, D ) that somebody who buys X also buys Y where X and Y are products/services. D is the set of customer attributes, e.g., gender, age, marital status etc Example: P ( chips beer ) = 0.7

10 Classification 11 Classification corresponds to learning a function (mapping) from input to output, where output is restricted to a finite number of classes (typically two but can have more classes) Such a learned function is called a classifier Input corresponds to any object represented by a set of attributes/features Such a input-output function can be modeled in different ways and is typically expressed by a set of model parameters Training of a classifier corresponds to learning optimum values of these model parameters Requires past data (input-output pairs) to do that Testing of classifier corresponds to predicting the class label (output) of an unseen data point given only the input (its features/attributes) Example: Credit scoring Two classes (high-risk, low-risk) Each data point is a customer having two features/attributes: income and savings Classification corresponds to differentiating between low-risk and high-risk customers from their income and savings Discriminant: IF income > θ 1 AND savings > θ 2 THEN low-risk ELSE high-risk Discriminant is a function that separates different classes

11 Classification 12 What are the model parameters in this example? In this example how the number of features/attributes is related to number of model parameters? Food for thought: What happens when you have large number of features? (we have seen in Bag of word format we can have thousands of features) Discriminant: IF income > θ 1 AND savings > θ 2 THEN low-risk ELSE high-risk Discriminant is a function that separates different classes

12 Classification: Applications 13 Aka Pattern recognition Face recognition: Pose, lighting, occlusion (glasses, beard), make-up, hair style Character recognition: Different handwriting styles. Speech recognition: Temporal dependency. Medical diagnosis: From symptoms to illnesses Biometrics: Recognition/authentication using physical and/or behavioral characteristics: Face, iris, signature, etc Outlier/novelty detection:

13 Face Recognition 14 Training examples of a person Test images ORL dataset, AT&T Laboratories, Cambridge UK

14 Regression 15 Regression corresponds to learning a function (mapping) from input to output, where output is any real number Such a learned function is called a regressor Input corresponds to any object represented by a set of attributes/features Such a input-output function can be modeled in different ways and is typically expressed by a set of model parameters Training of a regression model corresponds to learning optimum values of these model parameters Requires past data (input-output pairs) to do that Testing of trained regression model corresponds to predicting the scalar output value (output) of an unseen data point given only the input (its features/attributes) Example: Price of a car Input is car attributes Output is car price Regression model y = g (x q ) Model parameters are q In this example model parameters controls the orientation of the blue line

15 Regression Applications 16 Navigating a car: Angle of the steering Kinematics of a robot arm (x,y) α 1 = g 1 (x,y) α 2 = g 2 (x,y) α 2 α 1 Response surface design

16 Supervised Learning: Uses 17 Prediction of future cases: Use the rule to predict the output for future inputs Knowledge extraction: The rule is easy to understand Compression: The rule is simpler than the data it explains Outlier detection: Exceptions that are not covered by the rule, e.g., fraud

17 Unsupervised Learning 18 Learning what normally happens No output Clustering: Grouping similar instances Example applications Customer segmentation in CRM Image compression: Color quantization Bioinformatics: Learning motifs

18 Reinforcement Learning 19 Learning a policy: A sequence of outputs No supervised output but delayed reward Credit assignment problem Game playing Robot in a maze Multiple agents, partial observability,... Reinforcement learning a separate sub area of machine learning. Unfortunately we will not cover it in this class.

19 Resources: Datasets 20 UCI Repository: Statlib:

20 Resources: Journals 21 Journal of Machine Learning Research Machine Learning Neural Computation Neural Networks IEEE Trans on Neural Networks and Learning Systems IEEE Trans on Pattern Analysis and Machine Intelligence Journals on Statistics/Data Mining/Signal Processing/Natural Language Processing/Bioinformatics/...

21 Resources: Conferences 22 International Conference on Machine Learning (ICML) European Conference on Machine Learning (ECML) Neural Information Processing Systems (NIPS) Uncertainty in Artificial Intelligence (UAI) Computational Learning Theory (COLT) International Conference on Artificial Neural Networks (ICANN) International Conference on AI & Statistics (AISTATS) International Conference on Pattern Recognition (ICPR)...