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Lecture 2: Introduction to Machine Learning

Lecture 2: Introduction to Machine Learning. Machine Learning Definition . Field of study that gives computer the ability to learn without being explicitly programmed (Arthur Samuel, 1956)

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Lecture 2: Introduction to Machine Learning

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  1. Lecture 2: Introduction to Machine Learning

  2. Machine Learning Definition • 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) T: Play checkers P: % of games won E: Playing against self • Well defined learning task: <P, T, E>

  3. Well Defined Learning Task • Handwriting Recognition • Task T: recognizing and classifying handwritten words within images • Performance P: percent of words correctly classified • Training experience E: a database of written words with given classification

  4. Question • Suppose your email program watches which email you do and do not mark as spam and based on that learn how to better filter spam. What is the task in this setting • Classifying emails as spam or not spam • The number of emails correctly classifying as spam/not spam • Labelling emails as spam/ not spam • Non of above: This is not a machine learning problem

  5. Machine Learning Algorithms • Supervised Learning Algorithms • Unsupervised Learning Algorithms

  6. Supervised Learning • Right answers are given for inputs • Regression refers to predicting continuous valued output (e.g. price)

  7. Supervised Learning • Classification refers to predict discrete valued output (e.g. 0 or 1)

  8. Supervised Learning • More sophisticated features are: • Uniformity of cell size • Uniformity of cell shape, etc

  9. Question Suppose you are running a company and want to develop a learning algorithm to address each of two problems: • Problem 1: you have large inventory of identical items. You want to predict how many of items will sell over next 3 months. • Problem 2: you would like your program to examine individual customer accounts and for each account decide if it has been hacked or not. • Should you treat these as classification or regression problem ? • Treat both as classification problem • Treat problem 1 as classification and 2 as regression problem • Treat both as regression problem • Treat 1 as regression and 2 as classification problems

  10. Unsupervised Learning

  11. Unsupervised Learning Application

  12. Unsupervised Learning Application

  13. Unsupervised Learning Application Figure: DNA microarray data of individuals

  14. Unsupervised Learning Application Fridge, computer and dishwasher 3000 2500 2000 1500 1000 Average power consumptionn[W] 250 Fridge and computer 200 Fridge 150 100 50 0 0 50 100 150 200 250 300 350 400 450 Windows [#] Window size = 2 minutes

  15. 3000 State: S2 State: S5 2500 State: S6 State: S7 2000 Average power consumption [W] 1500 1000 500 0 310 320 330 340 350 360 370 380 390 Windows [#] Unsupervised Learning Application State sequence of diswasher Fridge, computer and dishwasher State sequence of fridge and computer S6 S7 S6 S7 S2 S5 S2 S5 3000 State: S1 160 State sequence of fridge State: S2 State: S2 State:S5 140 State: S3 S1 S4 S3 2500 State: S4 120 120 State: S5 State: S1 100 Average power consumption [W] State: S6 State: S3 2000 100 80 State: S7 State: S4 80 60 Average power consumption [W] 1500 40 60 20 40 1000 0 120 130 140 150 160 170 180 190 200 210 20 Average power consumptionn[W] Windows [#] 0 250 80 0 10 70 20 40 50 30 60 Windows [#] Fridge and computer 200 Fridge 150 100 50 0 0 50 100 150 200 250 300 350 400 450 Windows [#] Window size = 2 minutes

  16. Unsupervised Learning Applications

  17. Unsupervised Learning Application: Cocktail Party Problem

  18. Unsupervised Learning Application: Cocktail Party Algorithm

  19. Question • Of following examples, which one you address using unsupervised learning algorithm? • Given email labelled as spam/not spam, learn a spam filter • Given a set of news articles on the web, group them into set of articles about the same story • Given a database of customer data, automatically discover market segments and group customer into different market segments • Given a database of patients diagnosed as either having diabetes or not, learn to classify a new patients as either having a diabetes or not.

  20. Ungraded Assignment • Install Octave – an open source software or • Practice with: • Elementary operation: add, subtract, multiplication, power, divide, etc • Conditional operation: equal, not equal, greater, greater and equal to, etc • Logical operations: AND, OR, XOR, etc • Variable assignment • Vectors and matrices: defining vectors and matrices, ones, zeros, rand, eye • doc and help comand

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