Sampling Techniques COMPCSI 753: Algorithms for Massive Data Instructor: Ninh Pham University of Auckland Parts of this material are modifications of the lecture slides from http://mmds.org Designed for the textbook Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeff Ullman. Auckland, Aug 17, 2020 1 Outline Sampling from a data stream […]
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Part 1: Classwork HW assignment #5 Follow the videos and submit your spreadsheet solution to: 1. The NEES example (including sensitivity analysis). 2. The mobile oil company problem. 3. Keep the investment (example 2 in Utility slides). Part 2: Mobile oil company – sensitivity analysis For the Mobile oil company problem that we solved in
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Risk Preferences and Utility CIS 418 Source: S. Bodily, 2007 A dream of chances Those dreams are built from losing lottery tickets, by Brooklyn-based artists Adam Eckstrom and Lauren Was and it’s entitled Ghost of a Dream. The tickets were discarded by unlucky patrons. “Chance city” was built by the artist Jean Shin. Simon Business
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Decision Trees CIS 418 Business problems involve uncertainty: the NEES example NEES is a company that produces power. It is deciding how much to bid for the salvage rights to a grounded ship, the SS Kuniang. If successful, the ship could be repaired to haul coal for the company’s power stations. If the bid fails,
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CSC480/680: Midterm Exam Overview of concepts, algorithms and techniques that you are responsible for (in no particular order) [Please let me know if I have missed anything important!] 1. Concepts that you need to be able to describe and explain: 1.1 General Concepts: – Optimal Bayes Learning – Classification, Regression, Concept Learning, Multi-class learning –
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CSC 480: Previous Exam NOTE: This is a very long exam! Please, do as much as you can! I will adjust the scores afterwards! 1. Decision Trees (15 points: 7, 6, 2) Consider the following database of 10 voters described by three features: Age, Income, Gender and a binary class, Vote. ID Age Income Gender
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CSC480/680: Midterm Exam Overview of concepts, algorithms and techniques that you are responsible for (in no particular order) [Please let me know if I have missed anything important!] 1. Concepts that you need to be able to describe and explain: 1.1 General Concepts: – Optimal Bayes Learning – Classification, Regression, Concept Learning, Multi-class learning –
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CSC 480: Previous Exam NOTE: This is a very long exam! Please, do as much as you can! I will adjust the scores afterwards! 1. Decision Trees (15 points: 7, 6, 2) Consider the following database of 10 voters described by three features: Age, Income, Gender and a binary class, Vote. ID Age Income Gender
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Lab5_TreeLearning_As Tree Learning – implementation and application of decision trees¶ Copyright By PowCoder代写 加微信 powcoder Introduction¶ This notebook gives you the opportunity to implement some key components of decision tree learning and run your algorithm on a benchmark dataset. So restrictions will be made to simplify the problem. The notebook concludes by asking you to
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Kernel Methods COMP9417 Machine Learning & Data Mining Term 1, 2022 Adapted from slides by Dr Michael Copyright By PowCoder代写 加微信 powcoder 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
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