18-793 Image and Video Processing Submission instructions. Fall 2020 Submissions are due on Thursday 11/19 at 10.00pm ET Please upload scans of your solution in GradeScope (via Canvas) Homework 10 Instructions Please solve all non-MATLAB problems using only paper and pen, without resorting to a computer. Please show all necessary steps […]
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Support Vector Machines CS542 Machine Learning slides based on lecture by R. Urtasun http://www.cs.toronto.edu/~urtasun/courses/CSC2515/CSC2515_Winter15.html Support Vector Machine (SVM) • A maximum margin method, can be used for classification or regression • SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces • First,
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Unsupervised Learning III: Anomaly Detection Machine Learning Anomaly detection • What is anomaly detection? • Methods: – Density estimation – Detection by reconstruction – One-class SVM What is an anomaly? Anomaly Detection is • An unsupervised learning problem (data unlabeled) • About the identification of new or unknown data or signal that a machine learning
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CS.542 Machine Learning, Fall 2019, Prof. Saenko Additional Exam Practice Problems Note: Some of these sample problems had been used in past exams and are provided for practice in addition to the midterm practice and homework problems, which you should also review. A typical exam would have around 5 questions. The exam is closed book,
Neural Networks III Today: Outline • Neural networks cont’d • Types of networks: Feed-forward networks, convolutional networks, recurrent networks • ConvNets: multiplication vs convolution; filters (or kernels); convolutional layers; 1D and 2D convolution; pooling layers; LeNet, CIFAR10Net Machine Learning 2017, Kate Saenko 2 Neural Networks III Network Architectures Neural networks: recap 𝑥 h𝑖 hΘ(𝑥) Learn
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1. 2. http://cs229.stanford.edu/notes/cs229-notes3.pdf Slide credit: J. Sullivan, KTH For more reading refer to R. T. Rockarfeller (1970), Convex Analysis, Princeton University Press. Slide credit: J. Sullivan, KTH Slide credit: J. Sullivan, KTH Slide credit: J. Sullivan, KTH Slide credit: J. Sullivan, KTH Slide credit: J. Sullivan, KTH Slide credit: J. Sullivan, KTH Slide credit: J.
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CS.542 Machine Learning, Fall 2019, Prof. Saenko Additional Exam Practice Problems Note: Some of these sample problems had been used in past exams and are provided for practice in addition to the midterm practice and homework problems, which you should also review. A typical exam would have around 5 questions. The exam is closed book,
Lab 09 1. LDA: pset5 2. SVM: kernel trick Linear Discriminant Analysis Problem set 5 example Linear Discriminant Analysis from Lectures Linear Discriminant Analysis Linear Discriminant Analysis Slide Credit: Jia Li http://www.stat.psu.edu/∼jiali Linear Discriminant Analysis https://en.wikipedia.org/wiki/Pooled_variance Slide Credit: Jia Li http://www.stat.psu.edu/∼jiali Linear Discriminant Analysis scikit-learn.org Support Vector Machine Key points: • Difference between logistic regression
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Review CS542 Machine Learning Support Vector Machines CS542 Machine Learning slides based on lecture by R. Urtasun http://www.cs.toronto.edu/~urtasun/courses/CSC2515/CSC2515_Winter15.html Max Margin Classifier “Expand” the decision boundary to include a margin (until we hit first point on either side) Use margin of 1 Inputs in the margins are of unknown class Linear SVM Formulation This is the
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CS.542 Machine Learning, Fall 2019, Prof. Saenko Additional Exam Practice Problems Note: Some of these sample problems had been used in past exams and are provided for practice in addition to the midterm practice and homework problems, which you should also review. A typical exam would have around 5 questions. The exam is closed book,