Unsupervised Learning COMP9417 Machine Learning & Data Mining Term 1, 2021 Adapted from slides by Dr Michael Bain Aims This lecture will develop your understanding of unsupervised learning methods. Following it, you should be able to: • describe the problem of unsupervised learning • describe k-means clustering • describe Gaussian Mixture Models (GMM) • Outline […]
Learning Theory COMP9417 Machine Learning and Data Mining Term 2, 2020 COMP9417 ML & DM Learning Theory Term 2, 2020 1 / 78 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
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Ensemble Learning COMP9417 Machine Learning and Data Mining Term 2, 2020 COMP9417 ML & DM Ensemble Learning Term 2, 2020 1 / 70 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
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
Learning Theory COMP9417 Machine Learning and Data Mining Term 2, 2020 COMP9417 ML & DM Learning Theory Term 2, 2020 1 / 78 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
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Week 5: Probability Goals this week We are taking a turn this week towards probability and inference. You will likely find this week and the next (and the problem set) more difficult than before. But learning about probability is essential for understanding how the majority of data science and machine learning techniques really work. Even
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Image courtesy Unsplash / @mar5nsanchez Week 7/S1/2022 Data Governance Copyright By PowCoder代写 加微信 powcoder School of Computing and Information Systems Centre for AI & Digital Ethics The University of Melbourne marc.cheong [at] unimelb.edu.au Learning Outcomes 1. Define the concept of data governance from an organisational perspective. 2. Understand how laws such as the famous GDPR
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Lecture 21: Ethics in Machine Learning: Measuring and Mitigating Algorithmic Bias Introduction to Machine Learning Semester 1, 2022 Copyright @ University of Melbourne 2022. All rights reserved. No part of the publication may be reproduced in any form by print, photoprint, microfilm or any other means without written permission from the author. Copyright By PowCoder代写
INF 553: Foundations and Applications of Data Mining (Summer 2020) Yao-Yi Chiang Associate Professor (Research), Spatial Sciences Institute Associate Director, Integrated Media Systems Center Spatial Computing Lab University of Southern California Thanks for source slides and material to: J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets http://www.mmds.org What is Data Mining? About THIS
DATA 100 Final-Exam Fall 2020 INSTRUCTIONS Final-Exam This is your exam. Complete it either at exam.cs61a.org or, if that doesn’t work, by emailing course staff with your solutions before the exam deadline. This exam is intended for the student with email address . If this is not your email address, notify course staff immediately, as
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