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 program watches which 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 s as spam or not spam
The number of s correctly classifying as spam/not spam
Labelling s 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 Application50
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Fridge, computer and dishwasher
Window size = 2 minutes

15. Unsupervised Learning Application10 20 30 40 50 60 70 80
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State: S1
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State sequence of diswasher S6 S7
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 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