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Machine Learning CSE 681 CH1 - INTRODUCTION

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INTRODUCTION TO Machine Learning 2nd Edition ETHEM ALPAYDIN © The MIT Press, Textbook:

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Algorithms 3 Algorithm: To solve a problem on a computer, we need an algorithm. An algorithm is a sequence of instructions that transforms a given input to output. For example, one can devise an algorithm for sorting. For sorting, there may be various algorithms and we may be interested in finding the most efficient one, requiring the least number of instructions (time) or memory or both.

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Tasks without algorithms 4 For some tasks, however, we do not have an algorithm for example, to tell spam s from legitimate s. We know what the input is: an document that in the simplest case is a file of characters. We know what the output should be: a yes/no output indicating whether the message is spam or not. We do not know how to transform the input to the output. What can be considered spam changes in time and from individual to individual.

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Example Data 5 We can easily compile thousands of example messages some of which we know to be spam and what we want is to learn what consititutes spam from them. In other words, we would like the computer (machine) to extract automatically (learn) an algorithm (model) for this task. There are many applications for which we do not have an algorithm but do have example data.

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Machine Learning 6 Textbook definition: Machine learning is programming computers to optimize a performance criterion using example data or past experience. Tom Mitchells definition:

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What is learning 7 H. Simon: Any process by which a system improves its performance M. Minsky: Learning is making useful changes in our minds R. Michalsky: Learning is constructing or modifying representations of what is being experienced L. Valiant: Learning is the process of knowledge acquisition in the absence of explicit programming

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Why Learn ? 8 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)

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What We Talk About When We Talk AboutLearning 9 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 (www.amazon.com) Build a model that is a good and useful approximation to the data.

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Data Mining 10 Application of machine learning methods to large databases is called data mining. 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...

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What is Machine Learning? 11 Optimize a performance criterion using example data or past experience. Role of Statistics: Inference from a sample Role of Computer science: Efficient algorithms to Solve the optimization problem Representing and evaluating the model for inference

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What is Machine Learning? Source: Tom Mithcell 12

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Brief History 13 Studied ever since computers were invented (e.g. Samuel's checkers player) Very active in 1960s (neural networks) Died down in the 1970s Revival in early 1980s (decision trees, backpropagation, temporal difference learning) - coined as machine learning Exploded starting in the 1990s Now: very active research field, several yearly conferences (e.g., ICML, NIPS), major journals (e.g., Machine Learning, Journal of Machine Learning Research)

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ML niche is growing 14 Machine learning already the preferred approach to Speech recognition, Natural language processing Computer vision Medical outcomes analysis Robot control The time is right to study in the field! Lots of recent progress in algorithms and theory Flood of data to be analyzed ( Increased data capture, networking, new sensors) Software too complex to write by hand Computational power is available Growing demand for industrial applications

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ML Methods and Applications 15 Association Supervised Learning Classification Regression Unsupervised Learning Reinforcement Learning

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Learning Association (Unsupervised) Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0) 16 This is also called (Market) Basket Analysis. If people who buy X typically also buy Y, and if there is a customer who buys X and does not buy Y, he or she is a potential customer for Y. Learn Association Rules: Learn a conditional probability of the form P(Y | X) where Y is the product we would like to condition on X, which is a product or a set of products the customer has already purchased.

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Learning Associations 17 Basket analysis: P (Y | X ) probability that somebody who buys X also buys Y where X and Y are products/services. Example: P ( chips | beer ) = 0.7

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Applications of Association Rule Mining 18 Market-basket analysis helps in cross-selling products in a sales environment. Web usage mining: Items can be links on Web pages; Is a person more likely to click on a link having clicked on another link? We may download page corresponding to the next likely link to be clicked. If a customer has bought certain products on Amazon. com, what else could he/she be temped to buy? Intrusion detection: Each (or chosen) action is an item. If one action or a sequence of actions has been performed, is the person more likely to perform a few other instructions that may lead to intrusion?

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Classification 19 Example: Credit scoring We need a boundry (discriminant) 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

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Classification: Applications 20 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...

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Handwriting Recognition Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0) 21

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Plate Recognition Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0) 22

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Face Recognition 23 Training examples of a person Test images ORL dataset, AT&T Laboratories, Cambridge UK

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Natural Language Processing Applications 24 Parts-of-speech tagging, spam filtering, named entity recognition.

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Named Entity Recognition Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0) 25

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REGRESSION Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0) 26 Regression analysis includes any techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. Regression analysis helps us understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. Regression can be linear, quadratic, higher polynomial, log- based and exponential functions, etc.

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Regression Example: Price of a used car x : car attributes y : price y = g (x | ) g ( ) model, parameters 27 y = wx+w 0

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Example: Navigation of a mobile robot or an autonomous car The output is the angle by which the steering wheel should be turned each time to advance without hitting obstacles and deviating from the route. The inputs are obtained from sensors on the car: video camera, GPS, etc. Training data is collected by monitoring the actions of a human driver. 28

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Regression Applications 29 Navigating a car: Angle of the steering Kinematics of a robot arm α 1 = g 1 (x,y) α 2 = g 2 (x,y) α1α1 α2α2 (x,y)(x,y) Response surface design

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Supervised Learning: Uses 30 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

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UNSUPERVISED LEARNING Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0) 31 In supervised learning, we learn a mapping from input to output by analyzing examples for which correct values are given by a supervisor or a teacher or a human being. Examples of supervised learning: Classification, regression that we have already seen. In unsupervised learning, there is no supervisor. We have the input data only. The aim is to find regularities in the input. Examples: Association Rule Learning, Clustering, Outlier mining

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Unsupervised Learning 32 Learning what normally happens No output Clustering: Grouping similar instances Example applications Customer segmentation in CRM Image compression: Color quantization Bioinformatics: Learning motifs

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Unsupervised Learning: Clustering Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0) 33 Clustering: Given a set of data instances, organize them into a certain number of groups such that instances within a group are similar and instances in different groups are dissimilar. Bioinformatics: Clustering genes according to gene array expression data. Finance: Clustering stocks or mutual based on characteristics of company or companies involved. Document clustering: Cluster documents based on the words that are contained in them. Customer segmentation: Cluster customers based on demographic information, buying habits, credit

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REINFORCEMENT LEARNING Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0) 34 In some applications, the output of the system is a sequence of actions. A single action is not important alone. What is important is the policy or the sequence of correct actions to reach the goal. In reinforcement learning, reward or punishment comes usually at the very end or infrequent intervals. The machine learning program should be able to assess the goodness of policies; learn from past good action sequences to generate a policy.

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Reinforcement Learning Applications 35 Credit assignment problem Game playing Robot in a maze Multiple agents, partial observability,...

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Resources: Datasets 36 UCI Repository: UCI KDD Archive: Statlib: Delve:

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Resources: Journals 37 Journal of Machine Learning Research Machine Learning Neural Computation Neural Networks IEEE Transactions on Neural Networks IEEE Transactions on Pattern Analysis and Machine Intelligence Annals of Statistics Journal of the American Statistical Association...

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Resources: Conferences 38 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)...

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