Database Design True / False Multiple choice Copyright By PowCoder代写 加微信 powcoder Fill in the blanks Problem solving questions What to study Reading assignments (Chapter numbers vary based on edition) Additional Videos and Lecture Recordings Homework assignments and solutions Midterm exam Practice review questions and exercises at the end of chapters Project and Checkpoint documents […]
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Predictive Analytics – Week 6: Regularization Predictive Analytics Week 6: Regularization Copyright By PowCoder代写 加微信 powcoder Business Analytics, University of School Table of contents Ridge regression LASSO and other regularisation methods Recommended reading • Section 6.2, An Introduction to Statistical Learning with Applications in R by James et al.: easy to read, comes with R/Python
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ISOM3360 Data Mining for Business Analytics, Session 2 Data Mining Basics Instructor: Department of ISOM Spring 2022 Copyright By PowCoder代写 加微信 powcoder What is Data Mining? Data mining (knowledge discovery from data) Automatic extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from large amount of data Involves methods
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Machine Learning and Data Mining in Business Lecture 4: Practical Methodology Discipline of Business Analytics Copyright By PowCoder代写 加微信 powcoder Lecture 4: Practical Methodology Learning objectives • Interpretability. • Model selection. • Hyperparameter optimisation. • Feature selection. • Model stacking. • Model evaluation. Lecture 4: Practical Methodology for Machine Learning 1. Developing successful machine learning
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COMP3308/3608 Artificial Intelligence Weeks 5 Tutorial exercises Introduction to Machine Learning. K-Nearest Neighbor and 1R. Exercise 1 (Homework). K-Nearest Neighbor with numeric attributes Copyright By PowCoder代写 加微信 powcoder A lecturer has missed to mark one exam paper – Isabella’s. He doesn’t have time to mark it and decides to use the k-Nearest Neighbor algorithm to
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www.cardiff.ac.uk/medic/irg-clinicalepidemiology Classification Copyright By PowCoder代写 加微信 powcoder Information modelling & database systems rule-based systems: ‘if-then’ (deduction) supervised machine learning (induction) k–nearest neighbour inducing decision trees naïve Bayes support vector machines inter–annotator agreement evaluation: precision, recall, F-measure cross–validation platforms: Weka, Mechanical Turk, Crowdflower Dealing with complex information ‘divide’ & conquer ‘compartmentalise’ grouping similar or related things
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Classification methods applied to credit scoring: A systematic review and overall comparison Francisco Louzadaa Guilherme B. Fernandesc a Department of Applied Mathematics & Statistics, University of S ̃ao Paulo, S ̃ao Carlos, Brazil b Department of Statistics, Federal University of S ̃ao Carlos, S ̃ao Carlos, Brazil c P&D e Inovation in Analytics, Serasa-Experian, S
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DEPARTMENT OF MATHEMATICAL AND COMPUTATIONAL SCIENCES UNIVERSITY OF TORONTO MISSISSAUGA Class Location & Time Instructor Office Location Office Hours E-mail Address Course Web Site Teaching Assistant Course Description CSC338H5S LEC0101 Numerical Methods Course Outline – Winter 2020 Wed, 03:00 PM – 05:00 PM IB 235 Lisa Zhang DH3078 lczhang [at] cs [dot] toronto [dot] edu
What is Data Mining? in Databases I Data mining is the discovery of models for data • Statistical Modeling: Construction of a statistical model for the data • Machine Learning (training and test datasets) • Computational ( Summarizing or Extracting prominent features) 1 Big Data characteristics (five Vs) • Volume The quantity of generated and
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Map-Reduce Single Node Architecture CPU Memory Disk Machine Learning, Statistics “Classical” Data Mining J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org 2 Motivation: Google Example • 20+ billion web pages x 20KB = 400+ TB • 1 computer reads 30-35 MB/sec from disk – ~4 months to read the web • ~1,000
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