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What is Machine Learning

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Describes about Machine Learning

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What is Machine Learning

  1. 1. WHAT IS MACHINE LEARNING Bhaskara Reddy Sannapureddy, Senior Project Manager @Infosys, +91-7702577769
  2. 2. WHAT IS MACHINE LEARNING? Automating automation Getting computers to program themselves Writing software is the bottleneck Let the data do the work instead!
  3. 3. TRADITIONAL PROGRAMMING VS Computer Data Program Output Computer Data Output Program MACHINE LEARNING Traditional Programming Machine Learning
  4. 4. MAGIC? No, more like gardening Seeds = Algorithms Nutrients = Data Gardener = You Plants = Programs
  5. 5. SAMPLE APPLICATIONS Web search Computational biology Finance E-commerce Space exploration Robotics Information extraction Social networks Debugging [Your favorite area]
  6. 6. ML IN A NUTSHELL  Tens of thousands of machine learning algorithms  Hundreds new every year  Every machine learning algorithm has three components: • Representation • Evaluation • Optimization
  7. 7. REPRESENTATION  Decision trees  Sets of rules / Logic programs  Instances  Graphical models (Bayes/Markov nets)  Neural networks  Support vector machines  Model ensembles  Etc.
  8. 8. EVALUATION Accuracy Precision and recall Squared error Likelihood Posterior probability Cost / Utility Margin Entropy K-L divergence Etc.
  9. 9. OPTIMIZATION  Combinatorial optimization • E.g.: Greedy search  Convex optimization • E.g.: Gradient descent  Constrained optimization • E.g.: Linear programming
  10. 10. TYPES OF LEARNING  Supervised (inductive) learning • Training data includes desired outputs  Unsupervised learning • Training data does not include desired outputs  Semi-supervised learning • Training data includes a few desired outputs  Reinforcement learning • Rewards from sequence of actions
  11. 11. INDUCTIVE LEARNING  Given examples of a function (X, F(X))  Predict function F(X) for new examples X • Discrete F(X): Classification • Continuous F(X): Regression • F(X) = Probability(X): Probability estimation
  12. 12. SUPERVISED AND UNSUPERVISED LEARNING  Supervised learning • Decision tree induction • Rule induction • Instance-based learning • Bayesian learning • Neural networks • Support vector machines • Model ensembles • Learning theory  Unsupervised learning • Clustering • Dimensionality reduction
  13. 13. MACHINE LEARNING PROBLEMS
  14. 14. ML IN PRACTICE  Understanding domain, prior knowledge, and goals  Data integration, selection, cleaning, pre-processing, etc.  Learning models  Interpreting results  Consolidating and deploying discovered knowledge  Loop
  15. 15. CLUSTERING STRATEGIES  K-means • Iteratively re-assign points to the nearest cluster center  Agglomerative clustering • Start with each point as its own cluster and iteratively merge the closest clusters  Mean-shift clustering • Estimate modes of pdf  Spectral clustering • Split the nodes in a graph based on assigned links with similarity weights As we go down this chart, the clustering strategies have more tendency to transitively group points even if they are not nearby in feature space
  16. 16. THE MACHINE LEARNING FRAMEWORK  Apply a prediction function to a feature representation of the image to get the desired output: Slide credit: L. Lazebnik
  17. 17. THE MACHINE LEARNING FRAMEWORK y = f(x) output prediction function Image feature  Training: given a training set of labeled examples {(x1,y1), …, (xN,yN)}, estimate the prediction function f by minimizing the prediction error on the training set  Testing: apply f to a never before seen test example x and output the predicted value y = f(x) Slide credit: L. Lazebnik
  18. 18. THANK YOU

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