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Lecture 01 What is Machine Learning? An Overview. STAT 479: Machine Learning, Fall 2018 Sebastian Raschka http://stat.wisc.edu/~sraschka/teaching/stat479-fs2018/ 1
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About this Course When • Tue 8:00-9:15 am • Thu 8:00-9:15 am Where • SMI 331 Office Hours • Sebastian Raschka: Tue 3:00-4:00, Room MSC 1171 • Shan Lu (TA): Wed 3:00-4:00 pm, Room MSC B248 For details -> http://stat.wisc.edu/~sraschka/teaching/stat479-fs2018/ Sebastian Raschka STAT 479: Machine Learning FS 2018 !2
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ka STAT479 Fall 2018. Lecture #: Placeholder Page 2 “Machine learning is the hot new thing” — John L. Hennessy, President of Stanford (2000–2016) “A breakthrough in machine learning would be worth ten Microsofts” — Bill Gates, Microsoft Co-Founder Sebastian Raschka STAT 479: Machine Learning FS 2018 !4
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“Machine learning is the hot new thing” — John L. Hennessy, President of Stanford (2000–2016) “A breakthrough in machine learning would be worth ten Microsofts” — Bill Gates, Microsoft Co-Founder Sebastian Raschka STAT 479: Machine Learning FS 2018 !5
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rds, machine learning lets computers ”create” programs (often for makingWe develop (computer) programs to automate various kinds of processes. Originally devel-Department of Statistics oped as a subfield of Artificial Intelligence (AI), one of the goals behind machine learningUniversity of Wisconsin–Madison selves. Machine learning is turning data into programs.was to replace the need for developing computer programs ”manually.” If programs are a means to automate processes, we can think of machine learning as ”automating automa-http://stat.wisc.edu/⇠sraschka/teaching/stat479-fs2018/ rm tion.” In other words, machine learning lets computers ”create” programs (often for makingmachine learning was first coined by Arthur Lee Samuel in 19591. predictions) themselves. Machine learning is turning data into programs.Fall 2018 most every introductory machine learning resource is often accredited toIt is said that the term machine learning was first coined by Arthur Lee Samuel in 19591. er of the field of AI:One quote that almost every introductory machine learning resource is often accredited to1 What is Machine Learning? An Overview. Samuel, an pioneer of the field of AI: 1.1 Machine Learning – The Big Picture We develop (computer) programs to automate various kinds of processes. Originally devel-“Machine learning is the field of study that gives computers the ability to “Machine learning is the field of study that gives computers the ability tooped as a subfield of Artificial Intelligence (AI), one of the goals behind machine learninglearn without being explicitly programmed” learn without being explicitly programmed”was to replace the need for developing computer programs ”manually.” If programs are a— Arthur L. Samuel, AI pioneer, 1959 means to automate processes, we can think of machine learning as ”automating automa- tion.” In other words, machine learning lets computers ”create” programs (often for making predictions) themselves. Machine learning is turning data into programs.— Arthur L. Samuel, AI pioneer, 1959 (This is likely not an original quote but a paraphrased version of Samuel’s sentence ”Pro- gramming computers to learn from experience should eventually eliminate the need for muchIt is said that the term machine learning was first coined by Arthur Lee Samuel in 19591. One quote that almost every introductory machine learning resource is often accredited to of this detailed programming eSamuel, an pioneer of the field of AI: ↵ort.”) n original quote but a paraphrased version of Samuel’s sentence ”Pro- “Machine learning is the field of study that gives computers the ability to“The field of machine learning is concerned with the question of how to ters to learn from experience should eventually eliminate the need for muchlearn without being explicitly programmed”construct computer programs that automatically improve with experience” — Arthur L. Samuel, AI pioneer, 1959 ogramming e ↵ort.”)— Tom Mitchell, former chair of the Machine Learning department of (This is likely not an original quote but a paraphrased version of Samuel’s sentence ”Pro-Carnegie Mellon University gramming computers to learn from experience should eventually eliminate the need for much of this detailed programming e↵ort.”) “The field of machine learning is concerned with the question of how to1Arthur L Samuel. “Some studies in machine learning using the game of checkers”. In: IBM Journal of research and development“The field of machine learning is concerned with the question of how to3.3 (1959), pp. 210–229. construct computer programs that automatically improve with experience”construct computer programs that automatically improve with experience” — Tom Mitchell, former chair of the Machine Learning department of Carnegie Mellon University — Tom Mitchell, former chair of the Machine Learning department of 1Arthur L Samuel. “Some studies in machine learning using the game of checkers”. In: IBM Journal of Carnegie Mellon University research and development 3.3 (1959), pp. 210–229. Sebastian Raschka STAT 479: Machine Learning FS 2018 !6
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“A breakthrough in machine learning would be worth ten Microsofts” — Bill Gates, Microsoft Co-Founder “If software ate the world, models will run it” — Steven A. Cohen and Matthew W. Granade, The Wallstreet Journal, 2018 Sebastian Raschka STAT 479: Machine Learning FS 2018 !9
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in T, as measured by P, improves with experience E.” Figure 1: Machine learning vs. ”classic” programming.— Tom Mitchell, Professor at Carnegie Mellon University As an example, consider a handwriting recognition learning problem (from Mitchell’s book): it more concrete, Tom Mitchell’s quote from his Machine Learning book 2: • Task T: recognizing and classifying handwritten words within images “A computer program is said to• Performance measure P : percent of words correctly classifiedlearn from experience E with respect to some class of tasks• Training experienceT Eand performance measure: a database of handwritten words with given classificationsP, if its performance at tasks in T, as measured by P, improves with experience E.” 1.2 Applications of Machine Learning— Tom Mitchell, Professor at Carnegie Mellon University Email spam detection an example, consider a handwriting recognition learning problem (from Mitchell’s book): 2Tom M Mitchell et al. “Machine learning. 1997”. In: Burr Ridge, IL: McGraw Hill 45.37 (1997), pp. 870–877. • Task T: recognizing and classifying handwritten words within images • Performance measure P : percent of words correctly classified • Training experience E: a database of handwritten words with given classifications 2 Applications of Machine Learning ail spam detection 2Tom M Mitchell et al. “Machine learning. 1997”. In: Burr Ridge, IL: McGraw Hill 45.37 (1997), . 870–877. Sebastian Raschka STAT 479: Machine Learning FS 2018 !10
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A bit more concrete, Tom Mitchell’s quote from his Machin “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” — Tom Mitchell, Professor at Carnegie Mellon University“A computer program is said to learn from some class of tasks T and performance mea ple, consider a handwriting recognition learning problem (from Mitchell’s book):Handwriting Recognition Example:in T, as measured by P, improves with exp k T: recognizing and classifying handwritten words within images— Tom Mitchell, Profe formance measure P : percent of words correctly classified As an example, consider a handwriting recognition learningining experience E: a database of handwritten words with given classifications lications of Machine Learning • Task T: recognizing and classifying handwritten word am detection Mitchell et al. “Machine learning.• Performance measure1997”. In: Burr Ridge, IL: McGraw HillP : percent of words correctly c45.37 (1997), . • Training experience E: a database of handwritten wo Sebastian Raschka STAT 479: Machine Learning FS 2018 !11
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Categories of Machine Learning Labeled data Supervised Learning Direct feedback Predict outcome/future No labels/targets Unsupervised Learning No feedback Find hidden structure in data Decision process Reinforcement Learning Reward system Learn series of actions Sebastian Raschka STAT 479: Machine Learning FS 2018 !14
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Supervised Learning: Classification x 2 x 1 Sebastian Raschka STAT 479: Machine Learning FS 2018 !15
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Categories of Machine Learning Labeled data Supervised Learning Direct feedback Predict outcome/future No labels/targets Unsupervised Learning No feedback Find hidden structure in data Decision process Reinforcement Learning Reward system Learn series of actions Sebastian Raschka STAT 479: Machine Learning FS 2018 !17
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Unsupervised Learning -- Clustering x 2 x 1 Sebastian Raschka STAT 479: Machine Learning FS 2018 !18
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Unsupervised Learning -- Dimensionality Reduction Sebastian Raschka STAT 479: Machine Learning FS 2018 !19
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Categories of Machine Learning Labeled data Supervised Learning Direct feedback Predict outcome/future No labels/targets Unsupervised Learning No feedback Find hidden structure in data Decision process Reinforcement Learning Reward system Learn series of actions Sebastian Raschka STAT 479: Machine Learning FS 2018 !20
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