Reinforcement Learning II Recall: MDP notation • S – set of States • A – set of Actions • 𝑅𝑅: 𝑆𝑆 →R (Reward) • Psa – transition probabilities (𝑝𝑝(𝑠𝑠, 𝑎𝑎, 𝑠𝑠′) ∈ R) • 𝛾𝛾 – discount factor MDP = (S, A, R, Psa, 𝛾𝛾) Q-learning algorithm The agent interacts with the environment, updates Q […]
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Practical Advice for Applying Machine Learning Machine Learning Kate Saenko Outline Kate Saenko, CS542 Machine Learning • Machine learning system design • How to improve a model’s performance? • Feature engineering/pre-processing • Learning with large datasets Machine learning system design Practical Advice for Applying Machine Learning Example: Building a spam classifier From: cheapsales@buystufffromme.com To: ang@cs.stanford.edu
Reinforcement Learning Deep Mind’s bot playing Atari Breakout Machine Learning 2019, Kate Saenko 2 Reinforcement Learning • Plays Atari video games • Beats human champions at Poker and Go • Robot learns to pick up, stack blocks • Simulated quadruped learns to run Machine Learning 2019, Kate Saenko 3 What is reinforcement learning? Reinforcement Learning
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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,
Unsupervised Learning II Continuous Latent Variables 1 Today • Applications of clustering: vector quantization, data compression • Continuouslatentvariables:principalcomponent analysis 2 Unsupervised Learning II Applications of Clustering 3 Application of Clustering: Vector Quantization • • Compress an image using clustering Each {R, G, B} pixel value is an input vector 𝑥(𝑖) (255 x 255 x 255
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Supervised Learning I: Regression Today • Multivariate linear regression • Solution for SSD cost – Indirect – Direct • Maximum likelihood cost Hypothesis: 500 400 300 200 100 0 0 1000 2000 3000 Linear Regression ‘s: Parameters Cost Function: Multidimensional inputs Size (feet2) Number of Number of Age of home Price ($1000) bedrooms floors (years)
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CS.542 Machine Learning, Fall 2019, Prof. Saenko Machine Learning Midterm Practice Problems Some of these sample problems had been used in past exams and are provided for practice, in addition to the homework problems which you should also review. A typical exam would have around 5 questions worth a total of 100 points. The exam
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Semi-Supervised Learning Slides credit: Jerry Zhu, Aarti Singh Supervised Learning Feature Space Label Space Goal: Optimal predictor (Bayes Rule) depends on unknown PXY, so instead learn a good prediction rule from training data Learning algorithm Labeled 2 Labeled and Unlabeled data Human expert/ Special equipment/ Experiment “Crystal” “Needle” “Empty” “0” “1” “2” … “Sports” “News”
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