Computational Linguistics CSC 485 Summer 2020 10 10. Maximum Entropy Models Gerald Penn Department of Computer Science, University of Toronto (slides borrowed from Chris Manning and Dan Klein) Copyright © 2017 Gerald Penn. All rights reserved. Introduction Much of what we’ve looked at has been “generative” PCFGs, Naive Bayes for WSD In recent […]
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Hidden Markov Models in python¶ Here we’ll show how the Viterbi algorithm works for HMMs, assuming we have a trained model to start with. We will use the example in the JM3 book (Ch. 8.4.6). In [1]: import numpy as np Initialise the model parameters based on the example from the slides/book (values taken from figure).
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Artificial Intelligence Review What is an Agent? An entity □ situated: operates in a dynamically changing environment □ reactive: responds to changes in a timely manner □ autonomous:cancontrolitsownbehaviour □ proactive:exhibitsgoal-orientedbehaviour □ communicating: coordinate with other agents?? Examples: humans, dogs, …, insects, sea creatures, …, thermostats? Where do current robots sit on the scale? Lectures Environment
Unsupervised Learning COMP9417 Machine Learning and Data Mining Term 2, 2020 COMP9417 ML & DM Unsupervised Learning Term 2, 2020 1 / 91 Acknowledgements Material derived from slides for the book “Elements of Statistical Learning (2nd Ed.)” by T. Hastie, R. Tibshirani & J. Friedman. Springer (2009) http://statweb.stanford.edu/~tibs/ElemStatLearn/ Material derived from slides for the book
Automata, Computability and Complexity: Theory and Applications Elaine Rich Originally published in 2007 by Pearson Education, Inc. © Elaine Rich With minor revisions, July, 2019. Table of Contents PREFACE ………………………………………………………………………………………………………………………………..VIII ACKNOWLEDGEMENTS…………………………………………………………………………………………………………….XI CREDITS…………………………………………………………………………………………………………………………………..XII PARTI: INTRODUCTION…………………………………………………………………………………………………………….1 1 2 3 4 Why Study the Theory of Computation? ……………………………………………………………………………………………2 1.1 The Shelf Life of Programming Tools ………………………………………………………………………………………………2 1.2 Applications
Unsupervised Learning COMP9417 Machine Learning and Data Mining Term 2, 2020 COMP9417 ML & DM Unsupervised Learning Term 2, 2020 1 / 91 Acknowledgements Material derived from slides for the book “Elements of Statistical Learning (2nd Ed.)” by T. Hastie, R. Tibshirani & J. Friedman. Springer (2009) http://statweb.stanford.edu/~tibs/ElemStatLearn/ Material derived from slides for the book
TEXT MINING Applied Analytics: Frameworks and Methods 2 1 Outline ■ Examine the potential of analyzing unstructured data ■ Discuss applications of text analysis ■ Examine process of sentiment analysis ■ Use text as features in a predictive model ■ Review various methods used for text analysis 2 3 Business Decisions ■ Despite the overwhelming
CS 369 2020 Assignment 4 Due Wednesday June 10 10:00 pm In the first part of this assignment, we use a Hidden Markov Model to model secondary structure in protein sequences and implement a couple of algorithms we saw in lectures. In the second part, we simulate sequences down a tree according to the Jukes-Cantor
程序代写代做 Hidden Markov Mode html Bayesian DNA algorithm CS 369 2020 Assignment 4 Read More »
CS 369 2020 Assignment 4 Due Wednesday June 10 10:00 pm In the first part of this assignment, we use a Hidden Markov Model to model secondary structure in protein sequences and implement a couple of algorithms we saw in lectures. In the second part, we simulate sequences down a tree according to the Jukes-Cantor
程序代写代做 Hidden Markov Mode html Bayesian DNA algorithm CS 369 2020 Assignment 4 Read More »
CS 369: Viterbi and forward algorithms David Welch 9 April 2019 Processing math: 0% Viterbi algorithm (log units) The key part of the algorithm is calculating the recursion Vl(i + 1) = El(xi + 1) + max Think of V_k(i) = V(k,i) as a matrix. Row indices are states, column indices are positions along sequence.