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Machine Learning Overview Tamara Berg CS 590-133 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart Russell, Andrew Moore, Percy Liang, Luke Zettlemoyer, Rob Pless, Killian Weinberger, Deva Ramanan 1 Announcements • HW4 is due April 3 • Reminder: Midterm2 next Thursday – Next Tuesday’s lecture topics will not be included (but material will be on the final so attend!) • Midterm review – Monday, 5pm in FB009 Midterm Topic List Be able to define the following terms and answer basic questions about them: Reinforcement learning – – – – – – – Passive vs Active RL Model-based vs model-free approaches Direct utility estimation TD Learning and TD Q-learning Exploration vs exploitation Policy Search Application to Backgammon/Aibos/helicopters (at a high level) Probability – – – – – Random variables Axioms of probability Joint, marginal, conditional probability distributions Independence and conditional independence Product rule, chain rule, Bayes rule Midterm Topic List Bayesian Networks General – Structure and parameters – Calculating joint and conditional probabilities – Independence in Bayes Nets (Bayes Ball) Bayesian Inference – Exact Inference (Inference by Enumeration, Variable Elimination) – Approximate Inference (Forward Sampling, Rejection Sampling, Likelihood Weighting) – Networks for which efficient inference is possible Naïve Bayes – – – – – Parameter learning including Laplace smoothing Likelihood, prior, posterior Maximum likelihood (ML), maximum a posteriori (MAP) inference Application to spam/ham classification Application to image classification (at a high level) Midterm Topic List HMMs – – – – Markov Property Markov Chains Hidden Markov Model (initial distribution, transitions, emissions) Filtering (forward algorithm) Machine Learning – Unsupervised/supervised/semi-supervised learning – K Means clustering – Training, tuning, testing, generalization Machine learning Image source: https://www.coursera.org/course/ml Machine learning • Definition – Getting a computer to do well on a task without explicitly programming it – Improving performance on a task based on experience Big Data! What is machine learning? • Computer programs that can learn from data • Two key components – Representation: how should we represent the data? – Generalization: the system should generalize from its past experience (observed data items) to perform well on unseen data items. Types of ML algorithms • Unsupervised – Algorithms operate on unlabeled examples • Supervised – Algorithms operate on labeled examples • Semi/Partially-supervised – Algorithms combine both labeled and unlabeled examples Clustering – The assignment of objects into groups (aka clusters) so that objects in the same cluster are more similar to each other than objects in different clusters. – Clustering is a common technique for statistical data analysis, used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Euclidean distance, angle between data vectors, etc K-means clustering • Want to minimize sum of squared Euclidean distances between points xi and their nearest cluster centers mk D( X , M ) (x m ) i cluster k point i in cluster k k 2 Source: Hinrich Schutze Hierarchical clustering strategies • Agglomerative clustering • Start with each data point in a separate cluster • At each iteration, merge two of the “closest” clusters • Divisive clustering • Start with all data points grouped into a single cluster • At each iteration, split the “largest” cluster P P P Produces a hierarchy of clusterings P P Divisive Clustering • Top-down (instead of bottom-up as in Agglomerative Clustering) • Start with all data points in one big cluster • Then recursively split clusters • Eventually each data point forms a cluster on its own. Flat or hierarchical clustering? • For high efficiency, use flat clustering (e.g. k means) • For deterministic results: hierarchical clustering • When a hierarchical structure is desired: hierarchical algorithm • Hierarchical clustering can also be applied if K cannot be predetermined (can start without knowing K) Source: Hinrich Schutze Clustering in Action – example from computer vision Recall: Bag of Words Representation Represent document as a “bag of words” Bag-of-features models Slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba Bags of features for image classification 1. Extract features Bags of features for image classification 1. Extract features 2. Learn “visual vocabulary” Bags of features for image classification 1. Extract features 2. Learn “visual vocabulary” 3. Represent images by frequencies of “visual words” 1. Feature extraction … 2. Learning the visual vocabulary … 2. Learning the visual vocabulary … Clustering 2. Learning the visual vocabulary Visual vocabulary … Clustering Example visual vocabulary Fei-Fei et al. 2005 frequency 3. Image representation ….. Visual words Types of ML algorithms • Unsupervised – Algorithms operate on unlabeled examples • Supervised – Algorithms operate on labeled examples • Semi/Partially-supervised – Algorithms combine both labeled and unlabeled examples Example: Sentiment analysis http://gigaom.com/2013/10/03/stanford-researchers-to-open-source-model-they-say-has-nailed-sentiment-analysis/ http://nlp.stanford.edu:8080/sentiment/rntnDemo.html Example: Image classification input desired output apple pear tomato cow dog horse http://yann.lecun.com/exdb/mnist/index.html Surface wave magnitude Example: Seismic data Earthquakes Nuclear explosions Body wave magnitude The basic classification framework y = f(x) output classification function input • Learning: given a training set of labeled examples {(x1,y1), …, (xN,yN)}, estimate the parameters of the prediction function f • Inference: apply f to a never before seen test example x and output the predicted value y = f(x) Naïve Bayes classifier f ( x ) arg max y P( y | x ) arg max y P( y ) P( x | y ) arg max y P( y ) P( xd | y ) d A single dimension or attribute of x Example: Image classification Car Input: Image Representation Classifier (e.g. Naïve Bayes, Neural Net, etc Output: Predicted label Example: Training and testing Training set (labels known) Test set (labels unknown) • Key challenge: generalization to unseen examples Some classification methods Nearest neighbor Neural networks 106 examples Shakhnarovich, Viola, Darrell 2003 Berg, Berg, Malik 2005 … LeCun, Bottou, Bengio, Haffner 1998 Rowley, Baluja, Kanade 1998 … Support Vector Machines and Kernels Conditional Random Fields Guyon, Vapnik Heisele, Serre, Poggio, 2001 … McCallum, Freitag, Pereira 2000 Kumar, Hebert 2003 … Classification … more soon Types of ML algorithms • Unsupervised – Algorithms operate on unlabeled examples • Supervised – Algorithms operate on labeled examples • Semi/Partially-supervised – Algorithms combine both labeled and unlabeled examples Supervised learning has many successes • • • • • • recognize speech, steer a car, classify documents classify proteins recognizing faces, objects in images ... Slide Credit: Avrim Blum However, for many problems, labeled data can be rare or expensive. Need to pay someone to do it, requires special testing,… Unlabeled data is much cheaper. Slide Credit: Avrim Blum However, for many problems, labeled data can be rare or expensive. Need to pay someone to do it, requires special testing,… Unlabeled data is much cheaper. Speech Customer modeling Images Protein sequences Medical outcomes Web pages Slide Credit: Avrim Blum However, for many problems, labeled data can be rare or expensive. Need to pay someone to do it, requires special testing,… Unlabeled data is much cheaper. [From Jerry Zhu] Slide Credit: Avrim Blum However, for many problems, labeled data can be rare or expensive. Need to pay someone to do it, requires special testing,… Unlabeled data is much cheaper. Can we make use of cheap unlabeled data? Slide Credit: Avrim Blum Semi-Supervised Learning Can we use unlabeled data to augment a small labeled sample to improve learning? But maybe still has useful regularities that we can use. But unlabeled data is missing the most important info!! But… But… But… Slide Credit: Avrim Blum