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Machine Learning Overview
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  1. 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

  2. Announcements • HW4 is due April 3 • Reminder: Midterm2 next Thursday • Next Tuesday’s lecture topics will not be included (but materialwill be on the final so attend!) • Midterm review • Monday, 5pm in FB009

  3. 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 vsexploitation • 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

  4. 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)

  5. 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

  6. Machine learning Image source: https://www.coursera.org/course/ml

  7. Machine learning • Definition • Getting a computer to do well on a task without explicitly programming it • Improving performance on a task based on experience

  8. Big Data!

  9. 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.

  10. 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

  11. Clustering • The assignment of objects into groups (aka clusters) so that objects inthe same cluster are more similar to each other than objects indifferent 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.

  12. Euclidean distance, angle between data vectors, etc

  13. K-means clustering • Want to minimize sum of squared Euclidean distances between points xi and their nearest cluster centers mk

  14. Source: Hinrich Schutze

  15. 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

  16. P P P P Produces a hierarchy of clusterings

  17. P

  18. Divisive Clustering • Top-down (instead of bottom-up as in Agglomerative Clustering) • Start with all data pointsin one big cluster • Then recursively split clusters • Eventually each data pointforms a cluster on its own.

  19. 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

  20. Clustering in Action – example from computer vision

  21. Recall: Bag of Words Representation • Represent document as a “bag of words”

  22. Bag-of-features models Slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba

  23. Bags of features for image classification • Extract features

  24. Bags of features for image classification • Extract features • Learn “visual vocabulary”

  25. Bags of features for image classification • Extract features • Learn “visual vocabulary” • Represent images by frequencies of “visual words”

  26. 1. Feature extraction

  27. 2. Learning the visual vocabulary

  28. 2. Learning the visual vocabulary Clustering

  29. 2. Learning the visual vocabulary Visual vocabulary Clustering

  30. Example visual vocabulary Fei-Fei et al. 2005

  31. ….. 3. Image representation frequency Visual words

  32. 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