1. Introduction to Machine Learning Anjeli Singh Computer Science and Software Engineering April 28 th 2008

2. Overview What is Machine Learning Examples of Machine Learning Learning Associations Classification Regression Unsupervised Learning Reinforcement Learning Notes

3. What is Machine Learning? We store and process data Supermarket chain – Hundred of stores – Selling thousands of good to million of customers – Record the details: date, customer ID, goods, money – Gigabytes of data everyday – Turn this data into information to make prediction

4. What is Machine Learning? Do we know which people are likely to buy a particular product? Which author to suggest to people which enjoy reading?

5. What is Machine Learning? Answer is : NO

6. What is Machine Learning? Answer is :NO Because if we knew, we won’t need any data analysis. Go ahead and write code

7. What is Machine Learning? We can collect data Try to extract answers to these similar questions There is a process explains the data we observe Its not completely random E.g., Customer behavior People don’t buy random things E.g. when they buy beer they buy chips There are certain pattern in the data

8. What is Machine Learning? We can’t identify process completely We can construct good approximation Detect certain pattern and regularities This is the niche of Machine Learning We can use these patterns for prediction Assuming near future won’t be much different the past

9. What is Machine Learning? Application to large database: data mining Its an analogy for extracting minerals from Earth Large volume of data is processed – To construct a simple model with valuable use – Having a high predictive accuracy – Application : Retail, finance banks, manufacturing, medicine for diagnosis

10. What is Machine Learning? Its not a database problem To be intelligent, a system that is in changing environment should have the ability to learn System should learn and adapt Foresee and provide solution for all possible situations

11. Mathematics Problem 2 + 2=

12. Mathematics 2 + 2= 4

13. What is Machine Learning? How we did that ? Can we write a program to add two numbers ??

14. Faces Who is He ??

15. Faces Denzel Washington

16. What is Machine Learning? How do we acknowledge him ? Can you write a program for that???

17. What is Machine Learning? Pattern Recognition Problem Analyzing sample face images of that person Learning captures the pattern specific to the person Recognize by checking this person in a given image

18. What is Machine Learning? “Machine learning is programming computers to optimize a performance criterion using example data or past experience” Uses theory of statistics to build mathematical models Core task is to make inferences from a sample

19. What is Machine Learning? Role of Computer Science – Training, need efficient algorithms to solve optimization problems – To store and process massive data – Its representation and algorithmic solutions for inferences – Eg, the efficiency of learning and or inference algorithm its space and time complexity may be as imp as predictive accuracy

20. Examples of Machine Learning Learning Associations Classification Regression Unsupervised Learning Reinforcement Learning Notes

21. Learning Associations In case of retail: Basket Analysis Finding associations between product bought by customers If a customer buy X, typically also buy Y To find Potential Y customer Target them from cross selling

22. Learning Associations Association Rule – P(Y|X) where Y is the product we condition on X and X is the product which a customer has already purchased – Eg. P(chips|Beer) = 0.7 Then 70 % who buy beer also chips – Distinction Attribute: P(Y|X, D) D set of customer attributes Gender, age, marital status

23. Classification Credit card Example – Predict the chances of paying loan back – Customer will default /not pay the whole amount – Bank should get profit – Not inconvenience a customer over his financial capacity

24. Classification Credit Scoring – Calculate the risk given the amount and customer information – Customer information. Eg., Income, savings, profession, age, history etc. – Form a rule – Fits a Model to the past data – To calculate the risk for a new application

25. Classification Classes – Low Risk Savings – High Risk Ѳ2 Rule (Prediction) Ѳ1 Income If income> Ѳ1 AND savings> Ѳ2 THEN low-risk ELSE high-risk Example of discriminant Low Risk High Risk

26. Classification Decision Type – 0/1 (low-risk/high-risk) – P(Y|X) where Y customer attribute and X is 0/1 – P(Y=1|X=x) =0.8

27. Classification Pattern Recognition – Optical character Recognition – Recognizing character code from images – Multiple classes – Collection of strokes, has a regularity(not random dots) – Capture in learning a program – Sequence of characters eg. T?e word – Face recognition

28. Classification Speech recognition – Input is acoustic and classes are words Medical Diagnosis

29. Classification Knowledge Extraction – Learning a rule from data Compression – Fitting a rule to the data – Outlier Detection – Finding the instances that do not obey rule are exceptions – E.g. Fraud

30. Regression If something can predict price of a used car? – Input: Brand, year, engine capacity – Output: Car Price

31. Regression X denote car attribute Y be the price Survey past transaction Collect training data y:price Fitted function Y = wx+w 0x: milage

32. Supervised Learning There is an input and an output Learn mapping from input to output Model defined up to a set of parameters: y = g(x|Ѳ) g(.) is the model and Ѳ are its parameters y is the number of regressions or a class code (0/1)

33. Unsupervised Learning Only have input data Aim is to find regularities in the input There is a pattern

34. Unsupervised Learning To find the regularities in the input Structure in the input space Density Estimation Clustering Image Compression

35. Reinforcement Learning

36. Notes Evolution defines us We change our behavior To cope with change We don’t hardwire all sort of behavior Evolution gave us mechanism to learn We recognize, recall the strategy Learning has limitation also

37. Can we grow a third arm??

38. Evolution in ML Our aim is not understand the process underlying learning in human To build useful systems as in domain of engineering

39. Science fitting models of data Design experiments, observe and collect data Extract knowledge by finding out simple models, that explains the data Process of extracting general rules from a set of cases is Induction Going from particular observation to general description Statistics: inferenceLearning: estimation

40. Relevant Resources Journal of Machine Learning Research Neural Computation Neural Information Processing System (NIPS) Book: Introduction to Machine Learning by Ethem ALPAYDIN The MIT Press