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) 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>

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

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

Machine Learning Algorithms Supervised Learning Algorithms Unsupervised Learning Algorithms

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

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

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

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

Unsupervised Learning

Unsupervised Learning Application

Unsupervised Learning Application

Unsupervised Learning Application Figure: DNA microarray data of individuals

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

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

Unsupervised Learning Applications

Unsupervised Learning Application: Cocktail Party Problem

Unsupervised Learning Application: Cocktail Party Algorithm

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.

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