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Machine Learning

Machine Learning. Godfather to the Singularity. Traditional programming Machine learning. Computer. Data. Output. Program. Computer. Data. Program. Output. Machine Learning Applications. Visual Search, Waterfalls. User’s Query:. System’s Response:. User Feedback:. Yes. Yes.

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Machine Learning

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  1. Machine Learning Godfather to the Singularity

  2. Traditional programming Machine learning Computer Data Output Program Computer Data Program Output

  3. Machine Learning Applications

  4. Visual Search, Waterfalls User’s Query: System’s Response: User Feedback: Yes Yes Yes NO!

  5. Example: Boundary Detection • Is this a boundary?

  6. Learning a classifier Given some set of features with corresponding labels, learn a function to predict the labels from the features x x x x x x x o x o o o o x2 x1

  7. Sample Applications • Web search • Computational biology • Finance • E-commerce • Space exploration • Robotics • Information extraction • Social networks • Debugging • [Your favorite area]

  8. Other Applications of ML • The Google search engine uses numerous machine learning techniques • Spelling corrector: “spehlkorector”, “phonitickspewling”, “Brytney Spears”, “Brithney Spears”, … • Grouping together top news stories from numerous sources (news.google.com) • Analyzing data from over 3 billion web pages to improve search results • Analyzing which search results are most often followed, i.e. which results are most relevant

  9. Other Applications of ML (cont’d) • ALVINN, developed at CMU, drives autonomously on highways at 70 mph • Sensor input only a single, forward-facing camera

  10. Other Applications of ML (cont’d) • SpamAssassin for filtering spam e-mail • Data mining programs for: • Analyzing credit card transactions for anomalies • Analyzing medical records to automate diagnoses • Intrusion detection for computer security • Speech recognition, face recognition • Biological sequence analysis • Each application has its own representation for features, learning algorithm, hypothesis type, etc.

  11. How Do We Learn?

  12. General Inductive Learning(Scientific Method) Hypothesis Induction, generalization Actions, guesses Refinement Feedback, more observations Observations

  13. What is Machine Learning? • Building machines that automatically learn from experience • Important research goal of artificial intelligence • Applications: • Data mining programs that learn to detect fraudulent credit card transactions • Programs that learn to filter spam email • Autonomous vehicles that learn to drive on public highways

  14. Why use Machine Learning? • We cannot write the program ourselves • We don’t have the expertise (circuit design) • We cannot explain how (speech recognition) • Problem changes over time (packet routing) • Need customized solutions (spam filtering)

  15. Machine Learning • Optimize a criterion (reach a goal)using example data or past experience • Infer or generalize to new situations • Statistics: inference from a (small) sample • Probability: distributions and models • Computer Science: • Algorithms: solve the optimization problem efficiently • Data structures: represent the learned model

  16. Slide: Erik Sudderth

  17. Technologies • Supervised learning • Decision tree induction • Inductive logic programming • Instance-based learning • Bayesian learning • Neural networks • Support vector machines (SVM) • Model ensembles • Learning theory • Unsupervised learning • Clustering • Dimensionality reduction

  18. Regression Methods • k-Nearest Neighbors • Support Vector Machines • Neural Networks • Bayes Estimator

  19. Unsupervised Learning • No labels or feedback • Learn trends, patterns • Applications • Customer segmentation: e.g., targeted mailings • Image compression • Image segmentation: find objects • This course • k-means and EM clustering • Hierarchical clustering

  20. Reinforcement Learning • Learn a policy: sequence of actions • Delayed reward • Applications • Game playing • Balancing a pole • Solving a maze • This course • Temporal difference learning

  21. Hypothesis Type: Artificial Neural Network • Designed to simulate brains • “Neurons” (processing units) communicate via connections, each with a numeric weight • Learning comes from adjusting the weights

  22. b Perceptron(Simple Neural Net) • A single layer feed-forward network consists of one or more output neurons, each of which is connected with a weighting factor wij to all of the inputs xi. xi b

  23. Machine Learning vs. Expert Systems • ES: Expertise extraction tedious; ML: Automatic • ES: Rules might not incorporate intuition, which might mask true reasons for answer • E.g. in medicine, the reasons given for diagnosis x might not be the objectively correct ones, and the expert might be unconsciously picking up on other info • ML: More “objective”

  24. Machine Learning vs. Expert Systems (cont’d) • ES: Expertise might not be comprehensive, e.g. physician might not have seen some types of cases • ML: Automatic, objective, and data-driven • Though it is only as good as the available data

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