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- 1. WHAT IS MACHINE LEARNING Bhaskara Reddy Sannapureddy, Senior Project Manager @Infosys, +91-7702577769
- 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. TRADITIONAL PROGRAMMING VS Computer Data Program Output Computer Data Output Program MACHINE LEARNING Traditional Programming Machine Learning
- 4. MAGIC? No, more like gardening Seeds = Algorithms Nutrients = Data Gardener = You Plants = Programs
- 5. SAMPLE APPLICATIONS Web search Computational biology Finance E-commerce Space exploration Robotics Information extraction Social networks Debugging [Your favorite area]
- 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. REPRESENTATION Decision trees Sets of rules / Logic programs Instances Graphical models (Bayes/Markov nets) Neural networks Support vector machines Model ensembles Etc.
- 8. EVALUATION Accuracy Precision and recall Squared error Likelihood Posterior probability Cost / Utility Margin Entropy K-L divergence Etc.
- 9. OPTIMIZATION Combinatorial optimization • E.g.: Greedy search Convex optimization • E.g.: Gradient descent Constrained optimization • E.g.: Linear programming
- 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. 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. 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. MACHINE LEARNING PROBLEMS
- 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. 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. 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. 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. THANK YOU

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