EECS 3101 Prof. Andy Mirzaian Welcome to the beautiful and wonderful world of algorithms! 2 STUDY MATERIAL: • [CLRS] chapter 1 • Lecture Note 1 NOTE: • Material covered in lecture slides are as self contained as possible and may not necessarily follow the text book format. 3 Origin of the word Algorithm = […]
Submission The University of Adelaide, School of Computer Science Introduction to Statistical Machine Learning Semester 2, 2020 Assignment 2: Implementation of AdaBoost Instructions and submission guidelines: • You must sign an assessment declaration coversheet to submit with your assignment. • Submit your assignment via the Canvas MyUni. Reading With this assignment, you will see how
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ISOM3360 Data Mining for Business Analytics, Session 6&7 Model Evaluation Measures & Cost-sensitive Classification Instructor: Department of ISOM Spring 2022 Copyright By PowCoder代写 加微信 powcoder Example: A Regression Tree Problem: predict the price of used Toyota Corolla cars Available attributes: Age (in months), Weight (in kilograms), HP (Horsepower), KM (Accumulated kilometers), … – There are
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ISOM3360 Data Mining for Business Analytics, Session 3 Data Understanding and Preparation Instructor: Department of ISOM Spring 2022 Copyright By PowCoder代写 加微信 powcoder A Few Questions to Ask Yourself What is data mining? What are variables/target variable? What is supervised vs. unsupervised learning? What is classification vs. regression? What is mining phase vs. using phase?
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Jan. 25, 2022 Recitation 1 Jan. 25, 2022 1 / 26 Recitation 1 Statistical Learning Theory Intro to Gradient Descent Copyright By PowCoder代写 加微信 powcoder Motivation In data science, we generally need to Make a Decision on a problem. To do this, we need to understand The setup of the problem The possible actions The
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Cross-validation and the Bootstrap STAT318 — Data Mining Jiˇr ́ı Moravec University of Canterbury, Christchurch, Copyright By PowCoder代写 加微信 powcoder Some of the figures in this presentation are taken from “An Introduction to Statistical Learning, with applications in R” (Springer, 2013) with permission from the authors: G. James, D. Witten, T. Hastie and R. Tibshirani.
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Decision_Trees_from_Scratch Decision Trees¶ Decisions Trees are mainly used to solve classification problems. This notebook will cover how a decision tree is created, and will show how to plot the results of a decision tree. Copyright By PowCoder代写 加微信 powcoder This is based on sample code from Data Science from Scratch by , O’ , 2015.
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Sep 2020 Algorithms for Machine Learning Lecture 2: Graph Visualisation and Clustering Thomas Sauerwald University of Cambridge, Department of Computer Science email: thomas.sauerwald@cl.cam.ac.uk Outline Introduction to Spectral Graph Theory Graph Spectra and Graph Structure Graph Clustering Applications: Image Segmentation and Clustering Migration Networks Graph Visualisation and Spectral Clustering Introduction to Spectral Graph Theory 2 Landscape
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Coursework 3 Step 0: Setup Make sure to set up a dedicated python environment for this project. You can either use anaconda or venv to create a dedicated environment. With anaconda: conda create -n cw3 python=3.7 anaconda conda activate cw3 Copyright By PowCoder代写 加微信 powcoder # work work work conda deactivate with venv: python -m
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AFP Assignments (version: October 16, 2015) Atze Dijkstra (andAndresLo ̈h,DoaitseSwierstra,andothers) Summer – Fall 2015 Contents 1 Beginners exercises 9 1.1 Beginnerstraining,brief(*)…………………….. 9 1.1.1 Hello,world! ………………………… 9 1.1.2 Interactionwiththeoutsideworld……………… 10 1.1.3 Theexercise…………………………. 12 1.2 Beginnerstraining,extensive(*)………………….. 13 1.2.1 GettingstartedwithGHCi …………………. 13 1.2.2 Basicarithmetic……………………….. 14 1.2.3 Booleans…………………………… 15 1.2.4 Strings……………………………. 16 1.2.5 Types…………………………….. 18 1.2.6 Lists …………………………….. 19