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Lecture 2: Introduction to Machine Learning
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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)
Well defined learning task: <P, T, E>
T: Play checkersP: % of games wonE: Playing against self
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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
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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
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Machine Learning Algorithms
• Supervised Learning Algorithms• Unsupervised Learning Algorithms
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Supervised Learning
• Right answers are given for inputs• Regression refers to predicting continuous
valued output (e.g. price)
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Supervised Learning
• Classification refers to predict discrete valued output (e.g. 0 or 1)
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Supervised Learning
• More sophisticated features are:– Uniformity of cell size– Uniformity of cell shape, etc
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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
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Unsupervised Learning
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Unsupervised Learning Application
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Unsupervised Learning Application
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Unsupervised Learning Application
Figure: DNA microarray data of individuals
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Unsupervised Learning Application
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Unsupervised Learning Application
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Unsupervised Learning Applications
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Unsupervised Learning Application: Cocktail Party Problem
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Unsupervised Learning Application: Cocktail Party Algorithm
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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.
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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