School of Mathematics and Statistics Applied Data Science (MAST30034) Project 1: Quantitative Analysis Due date: 22nd of August 09:00 AM (AEST) Project Weight: 30% Project Overview Copyright By PowCoder代写 加微信 powcoder This project aims to make a quantitative analysis of the City Taxi and Limousine Service Trip Record Data. The dataset covers trips taken in […]
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Lead Research Scientist, Financial Risk Quantitative Research, SS&C Algorithmics Adjunct Professor, University of Toronto MIE1624H – Introduction to Data Science and Analytics Lecture 7 – Machine Learning University of Toronto March 1, 2022 Copyright By PowCoder代写 加微信 powcoder Machine learning Machine learning gives computers the ability to learn without being explicitly programmed ■ Supervised learning:
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MFIN6201 Lecture 2 Programming and Data Management Leo Liu February 26, 2020 About Me • Leo Liu • leo.liu@unsw.edu.au • Consultation hours: Tuesday 5-6pm from week 7 to week 10 • West Wing, Level 3 of Business School • Email me before you come, please • During exam time, I should be more flexible 2
Machine Learning and Data Mining in Business Semester 1, 2022 Regression Project: Airbnb Pricing Analytics 1. Overview In this project your team will analyse data from Airbnb rentals in Sydney to provide market advice to hosts, real estate investors, and other stakeholders. Your team will have two tasks: the first will be to build a
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Ensemble Learning COMP9417 Machine Learning and Data Mining Term 2, 2021 COMP9417 ML & DM Ensemble Learning Term 2, 2021 1 / 51 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, 2021 COMP9417 ML & DM Unsupervised Learning Term 2, 2021 1 / 76 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
Ensemble Learning COMP9417 Machine Learning and Data Mining Term 2, 2021 COMP9417 ML & DM Ensemble Learning Term 2, 2021 1 / 51 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, 2021 COMP9417 ML & DM Unsupervised Learning Term 2, 2021 1 / 76 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
SDDS Winter – 2021 School of Software Design and Data Science Milestone Worth Due Date Submission Required 1 10% 3 10% Introduction Assignment #1 Worth: 10% of final grade Account Ticketing System (Suggested Target: March 5th) (Suggested Target: March 15th / 16th) NO NO 2 40% March 12th (23:59 EST) YES 4 40% March 19th
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SDDS Winter – 2021 School of Software Design and Data Science Milestone Worth Due Date Submission Required 1 10% 3 10% Introduction Assignment #2 Worth: 15% of final grade Account Ticketing System (Suggested Target: March 23rd) (Suggested Target: April 2nd) NO NO 2 40% March 26th by 23:59 EST (end of Week 10) YES 4
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