Introduction to machine_learning

Introduction to machine_learning

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  1 Introduction toMachine Learning Kiran Lonikar
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  2 What islearning? Tom Mitchell: Learning is to improve some performance measure P of executing some task T with experience E. In plain English: Performing some task better with experience and training… Key Elements: • Remember or memorize the past experiences E • Generalize from the experiences E Observe how kids learn to read words: They make mistakes even when reading previously known words, then correct themselves. Especially happens when reading words with silent letters, and those ending with tion. Warning: This is a highly mathematical subject!
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  3 What isMachine Learning How would you build a computer program which “learns” from experiences? Generally a three phase process • Express Experience E mathematically: Build a set of features related to the experiences (Feature Extraction from raw data) • Memorize and Generalize: Build a mathematical model or set of rules from the experiences (training) • Apply the mathematical model to features of the future tasks
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  4 Machine Learningin Action… • Word Lens mobile app • OCR in web pages: http://newscarousel.herokuapp.com/scribble-js/Scribble.html
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  5 Types ofML Systems • Supervised Learning • Classification • Logistic Regression, SVM, NB, Decision Trees, ANN etc. • Regression • Recommender Systems* • User-user/item-item similarity, matrix factorization etc. • Unsupervised Learning • Clustering • K-means, Fuzzy K-Means, Model based (LDA) clustering etc. • Dimensionality reduction • Principal Component Analysis (PCA) • Anomaly Detection
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  6 Classification Identifyspeaker’s gender from the voice spectrum Amplitude Frequency • Training: Build a model using data: {(a1, f1, g1), (a2, f2, g2), … (am, fm, gm)} • Logistic Regression (LR): p(g = F | a, f; θ) = hθ(θ0 + θ1a + θ2f) • Decision Boundary: p < 0.5, g = M, else F
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  7 Logistic Regression • If we let • y = 1 when g = F, and y = 0 when g = M, and define vector x = [a, f] • and define a function hθ(x) = sigmoid(θT*x) where sigmoid(z) = 1/(1+e-z). It represents probability p(y=1|x,θ). • Cost J(θ) = -Σ(y*log(h) + (1-y)*log(1-h)) - λθTθ over all training examples for some λ. • Optimization algorithm (gradient descent): Obtain θ which minimizes J(θ). • Try to fit model θ to cross validation data, vary λ for optimum fitment. • Test model θ against test data: hθ(x) ≥ 0.5, predict gender = F, otherwise predict gender as M.
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  8 Recommender Systems • User j specifies ratings for item i: y(i,j) Training Data • Guess ratings for other items: The blanks Items Users 1 5 3 4 2 4 2 5 2 1 3 2 4 5 3 3 2 4 3 3 1 1 3 4 5 • Collaborative Filtering: k features of each item: • Feature vector xi for item i: {xi 1,xi 2, … xi k} • Parameter Vector θj for user j: {θj 1, θj 2, … θj k} • For user j’s estimated rating for item i: (θj)T xi
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  9 Recommender Systems • Learn xi and θj: • Given xi , minimize Σ((θj)T xi - y(i,j))2 for all i where user j has rated item i to find optimum θj. • Given θj, minimize Σ((θj)T xi - y(i,j))2 for all j where user j has rated item i to find optimum xi. • Simultaneously: minimize Σ((θj)T xi - y(i,j))2 for all (i,j) where user j has rated item i to find optimum θj and xi. • Find factors X and ϴ of ratings matrix Y such that Y ≈ X ϴT • Other Algorithms: user-user similarity, item-item similarity • Useful even when users are not humans, for e.g.. Wiki documents as users and links as items.
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  10 Clustering •Example: Top two occurring terms in documents • Training set: {x1, x2, x3, … xm}, vector xi • No labels (yi) specified #Term 2 #Term 1
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  11 Clustering: Applications • Computer Science • Document Clustering • Google news: Organizing similar news from different sources • News Categorizing • Social networks analysis • Features reduction: Speeding up ML pipelines • Cluster Centroids as new features • Image compression (Reduce number of colors): Pre-processing for faster, memory efficient computations • Deep Learning: Alternate supervised and unsupervised learning • Recommender Systems • Physics: • Astronomy • Particle physics • Market segmentation • http://en.wikipedia.org/wiki/Cluster_analysis#Applications
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  12 K-Means Clustering 1. Randomly choose initial cluster centroids #Term 2 #Term 1 2. Assign each training example to a cluster: Pick closest centroid 3. Move centroids: Re-compute centroids as average of training points assigned 4. Repeat 2, 3 for max iterations count or convergence
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  13 Popular MachineLearning Tools • Apache Mahout: • Various Recommender Systems, clustering, and classification algorithms • Java based, with some algorithms having Hadoop Map- Reduce implementations. Recently started spark implementations, with a new ML DSL. • Stable, widely used in production, community support. • R: • Popular in statistics world. Has its own language • GNU license • Spark MLLib, Mlbase(http://www.mlbase.org/) • Scala based. Runs on spark (in memory, distributed)
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  14 Popular MachineLearning tools • Weka: • Java based • GNU License • Vowpal Wabbit: http://hunch.net/~vw/, https://github.com/JohnLangford/vowpal_wabbit • Google Prediction API • http://en.wikipedia.org/wiki/Machine_learning#Soft ware
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  15 Machine LearningIn Action • Mobile: • Speech Recognition: Google Now, Siri • Languages/NLP: Google Translate • Vision: face recognition in cameras and online photos, OCR • Misc: Handwriting driven Myscript calculator and Stylus keyboard • Applications • OCR of printed documents and handwriting • Automatic tagging of photos based on similar faces • Biology and Medicine: • DNA analysis for likelihood of diseases, personalized drugs etc.
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  16 Resources •Online Courses: • Coursera: Machine Learning (Andrew Ng) • Coursera: Neural Networks for Machine Learning (Geoffrey Hinton) • Udacity: Intro to Artificial Intelligence (Peter Norvig, Sebastian Thrun) • CMU: Introduction to Machine Learning (Alex Smola) • Berkely: Scalable Machine Learning (Alex Smola) • Books: • Pattern Recognition and Machine Learning: Christopher Bishop • Machine Learning: Tom Mitchell • Mahout In Action • Artificial Intelligence: A modern approach (http://aima.cs.berkeley.edu/) • Machine Learning in Action
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  17 Resources •Quora: • http://www.quora.com/How-do-you-explain-Machine- Learning-and-Data-Mining-to-non-Computer-Science-people • http://www.quora.com/Machine-Learning • Misc.: • http://fastml.com/ • http://alex.smola.org/ • https://funnel.hasgeek.com/fifthel2014/1132-realizing-large-scale- distributed-deep-learning-ne • http://spark-summit.org/2014/agenda • Tutorial on HMM, Speech Recognition: Rabiner • Tesseract OCR library