Outline. Introduce yourself!! What is Machine Learning? What is CAP-5610 about? Class information and logistics

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1 Outine Introduce yoursef!! What is Machine Learning? What is CAP-5610 about? Cass information and ogistics Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

2 About the instructor: Name: Leonardo Bobadia, Ph.D B.E Computer Systems and Engineering. (Nationa University of Coombia) M.Sc Statistics (Nationa University of Coombia) Ph.D Computer Science (University of Iinois at Urbana-Champaign) Research interests: Robotics, Artificia Inteigence, Cyber-Physica Systems Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

3 About the instructor: Leonardo Bobadia, Assistant Professor Meeting times: Tuesday/Thursday 9:30am-10:45am Office:ECS 212b Phone: Tuesday/Thursday 11:00am-12:00pm or by appointment Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

4 Introduce yoursef! -Name -Year of PhD -Area -Advisor -Why are you interested in the cass? -Casses you have taken in Statistics and Linear Agebra Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

5 INTRODUCTION TO Lecture Sides for Machine Learning 2nd Edition ETHEM ALPAYDIN, modified by Leonardo Bobadia The MIT Press,

6 Why Learn? Machine earning is programming computers to optimize a performance criterion using exampe data or past experience. There is no need to earn to cacuate payro Learning is used when: Human expertise does not exist (navigating on Mars), Humans are unabe to expain their expertise (speech recognition) Soution changes in time (routing on a computer network) Soution needs to be adapted to particuar cases Lecture (user Notes for biometrics) E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

7 What We Tak About When We Tak About Learning Learning genera modes from a data of particuar exampes Data is cheap and abundant (data warehouses, data marts); knowedge is expensive and scarce. Exampe in retai: Customer transactions to consumer behavior: Peope who bought Bink aso bought Outiers ( Buid a mode that is a good and usefu approximation to the data. Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

8 Domains Machine earning is everywhere!! One of the most usefu areas of CS! Retai: Market basket anaysis, Customer reationship management (CRM) Finance: Credit scoring, fraud detection Manufacturing: Contro, robotics, troubeshooting Medicine: Medica diagnosis Teecommunications: Spam fiters, intrusion detection Bioinformatics: Motifs, aignment Web mining: Search engines Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

9 What is Machine Learning? Optimize a performance criterion using exampe data or past experience. Roe of Statistics: Inference from a sampe Roe of Computer science: Efficient agorithms to Sove the optimization probem Representing and evauating the mode for inference Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

10 Appications Association Supervised Learning Cassification Regression Unsupervised Learning Reinforcement Learning Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

11 Learning Associations Basket anaysis: P (Y X ) probabiity that somebody who buys X aso buys Y where X and Y are products/services. Exampe: P ( chips beer ) = 0.7 Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

12 Cassification Exampe: Credit scoring Differentiating between ow-risk and high-risk customers from their income and savings Discriminant: IF income > θ1 AND savings > θ2 THEN ow-risk ELSE high-risk Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

13 Cassification: Appications Aka Pattern recognition Face recognition: Pose, ighting, occusion (gasses, beard), make-up, hair stye Character recognition: Different handwriting styes. Speech recognition: Tempora dependency. Medica diagnosis: From symptoms to inesses Biometrics: Recognition/authentication using physica and/or behaviora characteristics: Face, iris, signature, etc... Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

14 Face Recognition Training exampes of a person Test images ORL dataset, AT&T Laboratories, Cambridge UK Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

15 Appications of Cassification Taken rom g Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

16 Appications of Cassification: POS Tagging Taken from Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

17 Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0) Regression Exampe: Price of a used car x : car attributes y : price y = g (x θ ) g ( ) mode, θ parameters y = wx+w0

18 Regression Appications Navigating a car: Ange of the steering Kinematics of a robot arm (x,y) α2 α1= g1(x,y) α2= g2(x,y) α1 Response surface design Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

19 Supervised Learning: Uses Prediction of future cases: Use the rue to predict the output for future inputs Knowedge extraction: The rue is easy to understand Compression: The rue is simper than the data it expains Outier detection: Exceptions that are not covered by the rue, e.g., fraud Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

20 Unsupervised Learning Learning what normay happens No output Custering: Grouping simiar instances Exampe appications Customer segmentation in CRM Image compression: Coor quantization Bioinformatics: Learning motifs Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

21 Unsupervised Learning Bioinformatics: Custering genes according to gene array expression data. Finance: Custering stocks or mutua based on characteristics of company or companies invoved Document custering: Custer documents based on the words that are contained in them. Customer segmentation: Custer customers based on demographic information, buying habits, credit information, etc. Companies advertise differenty to different customer segments. Outiers may form niche markets. Taken from Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

22 Unsupervised Learning Exampes Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

23 Reinforcement Learning In some appications, the output of the system is a sequence of actions. A singe action is not important aone. What is important is the poicy or the sequence of correct actions to reach the goa. In reinforcement earning, reward or punishment comes usuay at the very end or infrequent intervas. The machine earning program shoud be abe to assess the goodness of poicies ; earn from past good action sequences to generate a poicy. Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

24 Appications of Reinforcement Learning Game Paying: Games usuay have simpe rues and environments athough the game space is usuay very arge. A singe move is not of paramount importance; a sequence of good moves is needed. We need to earn good game paying poicy. Robot navigating in an environment: A robot is ooking for a goa ocation to charge, or to pick up trash, to pour a iquid, to hod a container or object. At any time, the robot can move in many in one of a number of directions, or perform one of severa actions. After a number of tria runs, it shoud earn the correct sequence of actions to reach the goa state from an initia state, and do it efficienty. Taken from

25 Resources: Datasets UCI Repository: UCI KDD Archive: Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

26 Course Logistics: Prerequisites Statistics and Linear Agebra usefu, but I can provide refreshers if needed A programming anguage Most important: Desire and motivation to earn and appy Machine Learning. Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

27 Course Logistics: Fina Project A research project, up to 2 students An opportunity to appy what you have earned Pick a probem that you find interesting Ideay shoud be a pubishabe effort 1) Project proposa. 2) Midterm progress. 3) Project Presentation. 4) Fina Report. I wi provide feedback a the way Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

28 Course Logistics: Homeworks Cheating No! Homework: Coaboration and study groups are encouraged; However, write your own soutions and program and do not use od soutions Midterm and Fina wi be based on homeworks Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

29 Course Logistics: Cass participation I wi put the corresponding sections of the book on the caendar. Pease read them before cass. Ask questions, participate, discuss in cass! Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

30 Course Logistics: Programming Languages Python: Interesting book Machine Learning: An Agorithmic Perspective R Octave Matab Java Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)

31 Next cass: Supervised Learning! Sections I2ML Lecture Notes for E Apaydın 2010 Introduction to Machine Learning 2e The MIT Press (V1.0)