Kernel Methods COMP9417 Machine Learning & Data Mining Term 1, 2021 Adapted from slides by Dr Michael Bain Aims This lecture will develop your understanding of kernel methods in machine learning. Following it you should be able to: – describe perceptron learning – describe learning with the dual perceptron – outline the idea of learning […]
Tree Learning COMP9417 Machine Learning and Data Mining Term 2, 2020 COMP9417 ML & DM Tree Learning Term 2, 2020 1 / 100 Acknowledgements Material derived from slides for the book “Machine Learning” by T. Mitchell McGraw-Hill (1997) http://www-2.cs.cmu.edu/~tom/mlbook.html Material derived from slides by Andrew W. Moore http:www.cs.cmu.edu/~awm/tutorials Material derived from slides by Eibe Frank
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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
Tree Learning COMP9417 Machine Learning and Data Mining Term 2, 2020 COMP9417 ML & DM Tree Learning Term 2, 2020 1 / 100 Acknowledgements Material derived from slides for the book “Machine Learning” by T. Mitchell McGraw-Hill (1997) http://www-2.cs.cmu.edu/~tom/mlbook.html Material derived from slides by Andrew W. Moore http:www.cs.cmu.edu/~awm/tutorials Material derived from slides by Eibe Frank
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Neural Learning COMP9417 Machine Learning & Data Mining Term 1, 2021 Adapted from slides by Dr Michael Bain Aims This lecture will develop your understanding of Neural Network Learning & will extend that to Deep Learning – describe Perceptrons and how to train them – relate neural learning to optimization in machine learning – outline
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Regression COMP9417 Machine Learning and Data Mining Term 2, 2020 COMP9417 ML & DM Regression Term 2, 2020 1 / 107 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 “Machine Learning:
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Classification (2) COMP9417 Machine Learning & Data Mining Term 1, 2021 Adapted from slides by Dr Michael Bain Aims This lecture will continue your exposure to machine learning approaches to the problem of classification. Following it you should be able to reproduce theoretical results, outline algorithmic techniques and describe practical applications for the topics: –
Name of Candidate: …………………………………………….. Student id: …………………………………………….. Signature: …………………………………………….. THE UNIVERSITY OF NEW SOUTH WALES Term 2, 2020 COMP9417 Machine Learning and Data Mining – Sample Final Examination 1. I ACKNOWLEDGE THAT ALL OF THE WORK I SUBMIT FOR THIS EXAM WILL BE COMPLETED BY ME WITHOUT ASSISTANCE FROM ANYONE ELSE. 2. TIME ALLOWED —
Neural Learning COMP9417 Machine Learning & Data Mining Term 1, 2021 Adapted from slides by Dr Michael Bain Aims This lecture will develop your understanding of Neural Network Learning & will extend that to Deep Learning – describe Perceptrons and how to train them – relate neural learning to optimization in machine learning – outline
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COMP8410 Data Mining 2020 Maximum marks Weight Length Layout Submission deadline Submission mode Estimated time Penalty for lateness First posted: Last modified: Questions to: Assignment 2 100 20% of the total marks for the course Maximum of 10 pages excluding cover sheet, bibliography and appendices. A4 margin, at least 11 point type size, use of
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