intro - Textbooks Machine Learning by Tom M. Mitchell CS...

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1 1 CS 464: Introduction to Machine Learning Aynur Dayanık Slides for Chapter 1 adapted from http://www.cs.waikato.ac.nz/ml/weka/book.html 2 Textbooks Machine Learning by Tom M. Mitchell http://www-2.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/mlbook.html Data Mining, Practical Machine Learning Tools and Techniques by I.H. Witten, E. Frank and M. A. Hall http://www.cs.waikato.ac.nz/ml/weka/book.html 3 Course Goals Introduce the basic machine learning algorithms Prepare you to be able to use ML tools Learn how to use WEKA to solve ML problems 4 Course Mechanics Project assignments Midterm (November 24) Final 5 What is Machine Learning? Using past experiences to improve future performance. For a machine, experiences come in the form of data. What does it mean to improve performance? Learning is guided by an objective, associated with a particular notion of loss to be minimized (or, equivalently, gain to be maximized). 6 Why Machine Learning? Recent progress in algorithms and theory Growing flood of online data Computational power is available Budding industry We need computers to make informed decisions on new, unseen data. Often it is too difficult to design a set of rules “by hand”. Machine learning is about automatically extracting relevant information from data and applying it to analyze new data.
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1 7 What’s it all about? Data vs information Data mining and machine learning Structural descriptions Rules: classification and association Decision trees Datasets Weather, contact lens, CPU performance, labor negotiation data, soybean classification Fielded applications Ranking web pages, loan applications, screening images, load forecasting, machine fault diagnosis, market basket analysis 8 Data vs. information Society produces huge amounts of data Sources: business, science, medicine, economics, geography, environment, sports, … Potentially valuable resource Raw data is useless: need techniques to automatically extract information from it Data: recorded facts Information: patterns underlying the data 9 Information is crucial Example 1: in vitro fertilization Given: embryos described by 60 features Problem: selection of embryos that will survive Data: historical records of embryos and outcome Example 2: cow culling Given: cows described by 700 features Problem: selection of cows that should be culled Data: historical records and farmers’ decisions 10 Data mining Extracting implicit, previously unknown, potentially useful information from data Needed: programs that detect patterns and regularities in the data Strong patterns good predictions Problem 1: most patterns are not interesting Problem 2: patterns may be inexact (or spurious) Problem 3: data may be garbled or missing 11 Machine learning techniques Algorithms for acquiring structural
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intro - Textbooks Machine Learning by Tom M. Mitchell CS...

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