Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University February 18, 2015 Today: • Graphical models • Bayes Nets: • Representing distributions • Conditional independencies • Simple inference • Simple learning Readings: • Bishop chapter 8, through 8.2 Graphical Models • Key Idea: – Conditional independence assumptions useful – but Naïve Bayes […]
Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 12, 2015 Today: • What is machine learning? • Decisiontreelearning • Courselogistics Readings: • “The Discipline of ML” • Mitchell,Chapter3 • Bishop,Chapter14.4 Machine Learning: Study of algorithms that • improve their performance P • at some task T • with experience E
Bias, Variance and Error Bias and Variance given algorithm that outputs estimate the bias of the estimator: the variance of estimator: e.g., estimator for probability n independent coin flips what is its bias? variance? for , we define: of heads, based on Bias and Variance given algorithm that outputs estimate for , we define: the
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Active Learning Literature Survey Burr Settles Computer Sciences Technical Report 1648 University of Wisconsin–Madison Updated on: January 26, 2010 Abstract The key idea behind active learning is that a machine learning algorithm can achieve greater accuracy with fewer training labels if it is allowed to choose the data from which it learns. An active learner
CISC 6525 Fall 2018 Artificial Intelligence Topics Covered 1. Computer Vision a. Virtual machine, use of OpenCv and use of ROS b. Image formation, image storage and image manipulation by computer c. Low level image operations: blurring, smoothing, sharpening, kernel operations d. Image segmentation, Cues for 3D structure, Stereovision, Optical flow e. Object Recognition 2.
CISC 6525 Artificial Intelligence Fall 2017 Final Exam Thursday December 14th 2017 In class, closed book and notes. Do all questions. Q1. Finding oil is an uncertain business. Oil is more likely Found in rocks of Type shale than in other sedimentary rocks, and more in rocks of Age younger than 100M years than in
1.dvi D RA FT Speech and Language Processing: An introduction to natural language processing, computational linguistics, and speech recognition. Daniel Jurafsky & James H. Martin. Copyright c© 2006, All rights reserved. Draft of June 25, 2007. Do not cite without permission. 1 INTRODUCTION Dave Bowman: Open the pod bay doors, HAL. HAL: I’m sorry Dave,
L15 – Inference in Bayes Nets EECS 391 Intro to AI Inference in Bayes Nets L15 Tue Oct 30 Recap: Variable elimination on the burglary network • We could do straight summation: • But: the number of terms in the sum is exponential in the non-evidence variables. • This is bad,
程序代写代做代考 Bayesian network Bayesian algorithm AI chain L15 – Inference in Bayes Nets Read More »
ENVM 3503 Semester 1 2003 1 1. Horse Race (10 marks) Let’s assume that there is a race between two horses: Fleetfoot and Dogmeat, and you want to determine which horse to bet on. Fleetfoot and Dogmeat have raced against each other on twelve previous occasions, all two-horse races. Of these last twelve races, Dogmeat
程序代写代做代考 Bayesian network Bayesian ENVM 3503 Semester 1 2003 Read More »
Jean Honorio Purdue University (originally prepared by Tommi Jaakkola, MIT CSAIL) CS373 Data Mining and� Machine Learning� Lecture 1 Course topics • Supervised learning - linear and non-linear classifiers, kernels - rating, ranking, collaborative filtering - model selection, complexity, generalization - conditional Random fields, structured prediction • Unsupervised learning, modeling - mixture models, topic models - Hidden Markov Models - Bayesian networks - Markov
程序代写代做代考 data mining Hidden Markov Mode Bayesian network Bayesian algorithm Jean Honorio Read More »