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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Algorithms for Data Science CSOR W4246 Computer Science Department Copyright By PowCoder代写 加微信 powcoder Columbia University Shortest paths in weighted graphs (Bellman-Ford, Floyd-Warshall) Shortest paths in graphs with non-negative edge weights (Dijkstra’s algorithm) Implementations Graphs with negative edge weights: why Dijkstra fails Single-source shortest paths (negative edges): Bellman-Ford A DP solution An alternative formulation of
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Preliminaries Who should take this class? • This is a difficult, math- and programming-intensive class geared primarily towards graduate students • Historically, much fewer undergraduates manage an A than graduate students Course Prerequisites • Linear algebra • Multivariate Calculus, including partial derivatives • Probability • Comfort with programming in Python • Fundamentals of Data Science
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DS-UA 201: Causal Inference: Regression Discontinuity Part 2 University Center for Data Science August 16, 2022 Copyright By PowCoder代写 加微信 powcoder Acknowledgement: Slides including material from DS-US 201 Fall 2021 offered by . Overview of the Sharp RD Estimator Our goal is to identify the local effect of assignment to treatment Di knowing that assignment
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Record Linkage Due date: 23:59 on Saturday of w7 (23:59) PERSON.csv Field Name PID FNAME LNAME SEX DOB_DAY DOB_MON DOB_YEAR PERSONS DWELLERS HSE_NUM STR_NAME POSTCODE CENSUS.csv Field Name CENSUS_ID FNAME LNAME SEX DOB_DAY DOB_MON DOB_YEAR PERSONS DWELLERS HSE_NUM STR_NAME POSTCODE Description PersonID First Name Last Name Gender (M/F) Day of birth Month of birth Year
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Data mining Prof. Dr. Matei Demetrescu Summer 2020 Statistics and Econometrics (CAU Kiel) Summer 2020 1 / 18 Today’s outline A brief overview 1 Find a needle in a haystack 2 The course 3 Practical details 4 Up next Statistics and Econometrics (CAU Kiel) Summer 2020 2 / 18 Find a needle in a haystack
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Assignment 3: Using the dplyr R package for bioprocess data CHEN40770: Data Science For Biopharmaceutical Manufacturing 14/10/2020 Contents 1 Introduction 1 2 Submitting your assignment 2 3 Questions 2 1 Introduction In our practical session we learned how to manipulate data with the dplyr package in R. For this assignment you will use the simulated
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ECE 209 Program 3: market Fall 2019 Due Friday, Oct 23 @ 11:59pm This programming assignment must be completed individually. Do not share your code with or receive code from any other student. Evidence of copying or other unauthorized collaboration will be investigated as a potential academic integrity violation. The minimum penalty for cheating on