Slide 1Source MaterialsWhy Study Machine Learning: A Few QuotesSlide 4Training DataMagic?Slide 8Definition: Machine Learning!Example 1: A Chess learning problemExample 2: Autonomous Vehicle ProblemWhen to use Machine Learning?Types of LearningExamples/Types of Machine Learning TasksSlide 15Classification Example: Spam FilteringClassification Example: Weather PredictionRegression example: Predicting Gold/Stock pricesSimilarity DeterminationCollaborative FilteringCollaborative FilteringCollaborative FilteringClustering: Discover Structure in dataSlide 24RepresentationEvaluationOptimizationMachine learning has grown in leaps and boundsWhat We’ll CoverMachine Learning: CS 6375IntroductionThe University of Texas at DallasSource Materials•T. Mitchell, Machine Learning,McGraw-Hill •C. Bishop, Pattern Recognition and Machine Learning, Springer •Kevin Murphy, Machine Learning: A probabilistic perspective•Class Notes/SlidesWhy Study Machine Learning:A Few Quotes•“A breakthrough in machine learning would be worthten Microsofts” (Bill Gates, Microsoft)•“Machine learning is the next Internet” (Tony Tether, Former Director, DARPA)•Machine learning is the hot new thing” (John Hennessy, President, Stanford)•“Web rankings today are mostly a matter of machine learning” (Prabhakar Raghavan, Former Dir. Research, Yahoo)•“Machine learning is going to result in a real revolution” (Greg Papadopoulos, CTO, Sun)Traditional Programming Machine Learning•Getting computers to program themselves•Writing software is the bottleneck, let data do the workHumanRequirementsProgramComputerDataInputOutputRequirements and data change oftenMachine LearningRequirementsProgramComputerDataInputOutputTraining DataTwo Classes:{Yes,No}Training ExampleMagic? No, more like gardening•Seeds = Algorithms•Nutrients = Data•Gardener = You•Plants = Programs•T. Mitchell: Well posed machine learning–Improving performance via experience–Formally, A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, it its performance at tasks in T as measured by P, improves with experience.•H. Simon–Learning denotes changes in the system that are adaptive in the sense that they enable the system to do the task or tasks drawn from the same population more efficiently and more effectively the next time. The ability to perform a task in a situation which has never been encountered before (Learning = Generalization)Definition: Machine Learning!Definition: Machine Learning!•Pioneer machine learning researcher Arthur Samuel defined machine learning as: “the field of study that gives computers the ability to learn without being explicitly programmed”.Example 1: A Chess learning problem•Task T: playing chess•Performance measure P: percent of games won against opponents•Training Experience E: playing practice games against itselfExample 2: Autonomous Vehicle Problem•Task T: driving on a public highway/roads using vision sensors•Performance Measure P: percentage of time the vehicle is involved in an accident•Training Experience E: a sequence of images and steering commands recorded while observing a human driverWhen to use Machine Learning?•Human expertise is absent–Example: navigating on mars•Humans are unable to explain their expertise–Example: vision, speech, language•Requirements and data change over time–Example: Tracking, Biometrics, Personalized fingerprint recognition•The problem or the data size is just too large–Example: Web SearchTypes of Learning•Supervised (inductive) learning–Training data includes desired outputs•Unsupervised learning–Training data does not include desired outputs–Find hidden/interesting structure in data•Semi-supervised learning–Training data includes a few desired outputs•Reinforcement learning–the learner interacts with the world via “actions” and tries to find an optimal policy of behavior with respect to “rewards” it receives from the environmentExamples/Types of Machine Learning Tasks•Forecasting or Prediction–Stock price of Google tomorrow?•Classification and Regression–Is Ana credit-worthy?–What is Ana’s credit score?•Ranking–How to rank images that contain “An awesome machine learning model”?•Outlier/Anomaly/Fraud detection–Is it Ana” using the credit card in Mexico or is it someone else?•Finding patterns–Almost 60% of shoppers buy Diapers and Milk together!Machine Learning: ApplicationsExamples of what you will study in class in action!Classification Example: Spam FilteringClassify as “Spam” or “Not Spam”Classification Example: Weather PredictionRegression example: Predicting Gold/Stock pricesGiven historical data on Gold prices, predict tomorrow’s price!Good ML can make you rich (but there is still some risk involved).Similarity DeterminationCollaborative Filtering•The problem of collaborative filtering is to predict how well a user will like an item that he has not rated given a set of historical preference judgments for a community of users.Collaborative FilteringCollaborative FilteringClustering: Discover Structure in dataRepresentation•Decision trees•Sets of rules / Logic programs•Instances•Graphical models (Bayes/Markov nets)•Neural networks•Support vector machines•Model ensembles•Etc.Evaluation•Accuracy•Precision and recall•Squared error•Likelihood•Posterior probability•Cost / Utility•Margin•Entropy•K-L divergence•Etc.Optimization•Combinatorial optimization–E.g.: Greedy search•Convex optimization–E.g.: Gradient descent•Constrained optimization–E.g.: Linear programmingMachine learning has grown in leaps and bounds•The main approach for–Speech Recognition–Robotics–Natural Language Processing–Computational Biology–Sensor networks–Computer Vision–Web–And so onAlice/Bob says: I know machine learning very well!Potential Employer: You are hired!!!What We’ll Cover•Supervised learning: Decision tree induction, Rule induction, Instance-based learning, Bayesian learning, Neural networks, Support vector machines, Linear Regression, Model ensembles, Graphical models, Learning theory, etc.•Unsupervised learning: Clustering, Dimensionality reduction•Reinforcement learning: Markov Decision Processes, Q-learning, etc.•General machine learning concepts and techniques: Feature selection, cross-validation, maximum
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