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Tree-Based Methods April 15, 2020 April 15, 2020 1 / 106 1 The basics of decision trees. 2 Bagging, random forests and boosting April 15, 2020 2 / 106 About this chapter • Decisions trees: splitting each variable sequentially, creating rectugular regions. • Making fitting/prediction locally at each region. • It is intuitive and easy
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PAPER CODE NO. COMP532 EXAMINER: Dr. Shan Luo Tel. No. 58407 DEPARTMENT: Computer Science SECOND SEMESTER EXAMINATIONS 2019/20 Machine Learning and BioInspired Optimization INSTRUCTIONS TO CANDIDATES 1. Answer FOUR questions. If you attempt to answer more questions than the required number of questions, the marks awarded for the excess question answered will be discarded (starting
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PAPER CODE NO. COMP532 EXAMINER: Dr. Shan Luo Tel. No. 58407 DEPARTMENT: Computer Science SECOND SEMESTER EXAMINATIONS 2019/20 Machine Learning and BioInspired Optimization INSTRUCTIONS TO CANDIDATES 1. Answer FOUR questions. If you attempt to answer more questions than the required number of questions, the marks awarded for the excess question answered will be discarded (starting
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Using machine learning results for calibration of stochastic volatility models. Blanka Horvath Department of Mathematics King’s College London 7CCMFM18 Machine Learning Lecture 9th March, 2020 Original paper available on SSRN:3322085 Contents of this lecture B. Horvath Deep Learning Volatility Contents of this lecture How to calibrate stochastic volatility models (including rough volatility models) using
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7CCMFM18 MACHINE LEARNING Dr Blanka Horvath Department of Mathematics, King’s College London Lecture notes Updated: 2 March 2020 ●●●●● ●●●●●●● ●●●●●●● ●● ●●●●●●● ●● ●●●●●●● ●●● ●●●●●●● ●● ●●●●●●● ●● ●●●●●●● ●●●●●●● ●●●●● B. Horvath 7CCMFM18 Machine Learning Spring 2020 Contents Outline of the module and practical matters 1 Introduction 3 5 1.1 Ahelicoptertourofdeeplearning ………………..