Introduction. Welcome. Machine Learning
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1 Introduction Welcome Machine Learning
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4 SPAM
5 Machine Learning -Grew out of work in AI -New capability for computers Examples: -Database mining Large datasets from growth of automation/web. E.g., Web click data, medical records, biology, engineering - Applications can t program by hand. E.g., Autonomous helicopter, handwriting recognition, most of Natural Language Processing (NLP), Computer Vision.
6 Machine Learning -Grew out of work in AI -New capability for computers Examples: -Database mining Large datasets from growth of automation/web. E.g., Web click data, medical records, biology, engineering - Applications can t program by hand. E.g., Autonomous helicopter, handwriting recognition, most of Natural Language Processing (NLP), Computer Vision.
7 Machine Learning -Grew out of work in AI -New capability for computers Examples: -Database mining Large datasets from growth of automation/web. E.g., Web click data, medical records, biology, engineering - Applications can t program by hand. E.g., Autonomous helicopter, handwriting recognition, most of Natural Language Processing (NLP), Computer Vision.
8 Machine Learning -Grew out of work in AI -New capability for computers Examples: -Database mining Large datasets from growth of automation/web. E.g., Web click data, medical records, biology, engineering - Applications can t program by hand. E.g., Autonomous helicopter, handwriting recognition, most of Natural Language Processing (NLP), Computer Vision. - Self-customizing programs E.g., Amazon, Netflix product recommendations
9 Machine Learning -Grew out of work in AI -New capability for computers Examples: -Database mining Large datasets from growth of automation/web. E.g., Web click data, medical records, biology, engineering - Applications can t program by hand. E.g., Autonomous helicopter, handwriting recognition, most of Natural Language Processing (NLP), Computer Vision. - Self-customizing programs E.g., Amazon, Netflix product recommendations - Understanding human learning (brain, real AI).
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11 Introduction What is machine learning Machine Learning
12 Machine Learning definition
13 Machine Learning definition Arthur Samuel (1959). Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed.
14 Machine Learning definition Arthur Samuel (1959). Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed.
15 Machine Learning definition Arthur Samuel (1959). Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed. Tom Mitchell (1998) Well-posed Learning Problem: A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.
16 A computer program is said to learnfrom experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. Suppose your program watches which s you do or do not mark as spam, and based on that learns how to better filter spam. What is the task T in this setting? Classifying s as spam or not spam. Watching you label s as spam or not spam. The number (or fraction) of s correctly classified as spam/not spam. None of the above this is not a machine learning problem.
17 A computer program is said to learnfrom experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. Suppose your program watches which s you do or do not mark as spam, and based on that learns how to better filter spam. What is the task T in this setting? Classifying s as spam or not spam. Watching you label s as spam or not spam. The number (or fraction) of s correctly classified as spam/not spam. None of the above this is not a machine learning problem.
18 A computer program is said to learnfrom experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. Suppose your program watches which s you do or do not mark as spam, and based on that learns how to better filter spam. What is the task T in this setting? Classifying s as spam or not spam. Watching you label s as spam or not spam. The number (or fraction) of s correctly classified as spam/not spam. None of the above this is not a machine learning problem.
19 Machine learning algorithms: - Supervised learning - Unsupervised learning Others: Reinforcement learning, recommender systems. Also talk about: Practical advice for applying learning algorithms.
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21 Introduction Supervised Learning Machine Learning
22 Housing price prediction. 400 Price ($) in 1000 s Size in feet 2 Supervised Learning right answers given Regression:Predict continuous valued output (price)
23 Breast cancer (malignant, benign) Malignant? 1(Y) 0(N) Tumor Size Classification Discrete valued output (0 or 1) Tumor Size
24 Age - Clump Thickness - Uniformity of Cell Size - Uniformity of Cell Shape Tumor Size
25 You re running a company, and you want to develop learning algorithms to address each of two problems. Problem 1: You have a large inventory of identical items. You want to predict how many of these items will sell over the next 3 months. Problem 2: You d like software to examine individual customer accounts, and for each account decide if it has been hacked/compromised. Should you treat these as classification or as regression problems? Treat both as classification problems. Treat problem 1 as a classification problem, problem 2 as a regression problem. Treat problem 1 as a regression problem, problem 2 as a classification problem. Treat both as regression problems.
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27 Introduction Unsupervised Learning Machine Learning
28 Supervised Learning x 2 x 1
29 Unsupervised Learning x 2 x 1
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32 Genes Individuals [Source: Daphne Koller]
33 Genes Individuals [Source: Daphne Koller]
34 Organize computing clusters Social network analysis Market segmentation Image credit: NASA/JPL-Caltech/E. Churchwell (Univ. of Wisconsin, Madison) Astronomical data analysis
35 Cocktail party problem Speaker #1 Microphone #1 Speaker #2 Microphone #2
36 Microphone #1: Microphone #2: Output #1: Output #2: Microphone #1: Microphone #2: Output #1: Output #2: [Audio clips courtesy of Te-Won Lee.]
37 Cocktail party problem algorithm [W,s,v] = svd((repmat(sum(x.*x,1),size(x,1),1).*x)*x'); [Source: Sam Roweis, Yair Weiss & Eero Simoncelli]
38 Of the following examples, which would you address using an unsupervisedlearning algorithm? (Check all that apply.) Given labeled as spam/not spam, learn a spam filter. Given a set of news articles found 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 customers into different market segments. Given a dataset of patients diagnosed as either having diabetes or not, learn to classify new patients as having diabetes or not.
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