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F71SM STATISTICAL METHODS 5 MULTIVARIATE DISTRIBUTIONS AND LINEAR COMBINATIONS 5.1 Introduction — several random variables at once The concepts and descriptions of random variables introduced in section 3 all extend to dis- tributions of several random variables defined simultaneously on a joint sample space — these give us vector random variables, or multivariate distributions. In […]
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################################################### # Computer Lab 2 – F71SM ################################################### ## In the tasks below, some (but not all) R code is given ## for guidance. In some places the code is also incomplete, ## so you will need to figure out how to fill in the details. ## Make sure you use the help menus in
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################################################### # Computer Lab 3 – F71SM ################################################### ## In the tasks below, fill in any missing code as ## required. ## Make sure you use the help menus in R (e.g.”?mean” ## will open up a help window for “mean()”). ## In some cases, the numerical answers you will find ## may be slightly
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F71SM STATISTICAL METHODS Tutorial on Section 1 DATA SUMMARY 1. The sums insured (£), in order, under 7 policies are as follows. 1,216 1,543 1,616 1,684 1,823 2,015 15,042 Calculate the sample mean and standard deviation of the sums insured (a) including the observation 15,042 [x ̄ = £3562.7, s = £5067.9] (b) excluding the
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CSC 311: Introduction to Machine Learning Lecture 3 – Linear Classification Based on slides by Amir-massoud Farahmand & Emad A.M. Andrews Intro ML (UofT) CSC311-Lec3 1 / 39 Last class, we discussed linear regression, and used a modular approach to machine learning algorithm design: chooseamodel: y=f(x)=w⊤x+b choose a loss: L(y, t) = 12 (y −
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CSC 311: Introduction to Machine Learning Lectures 5 and 6 – Probabilistic Models Based on slides by Amir-massoud Farahmand & Emad A.M. Andrews Intro ML (UofT) CSC311-Lec5&6 1 / 55 Goal: A more focused discussion on models that explicitly represent probabilities MLE review Discriminative vs. Generative models Generative models Na ̈ıveBayes Gaussian Discriminant
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CIND110 – Lab 05 CIND110 – Data Organization for Data Analysts Lab 05 – Relational Algebra Expressions 1 Running the MySQL Service 2 2 The Working Database Schema 3 3 Unary Relational Operations 4 3.1 3.2 3.3 TheSELECTOperation(σ)………………………… 4 ThePROJECTOperation(π)………………………… 7 TheRENAMEOperation(ρ) ………………………… 9 4 Set Theory Operations 11 4.1 TheUNIONOperation(∪)…………………………. 11 4.2 TheINTERSECTOperation(∩) ……………………….
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CIND110 – Data Organization for Data Analysts Lab 03 – Basic SQL Operations 1 Running the MySQL Service 2 Creating a Database 3 Designing Tables 4 Inserting/Removing Records/Rows 5 Altering Attributes/Columns 6 Updating Data 7 Cloning Tables 8 Integrity Constraints 11 13 16 17 CIND110 – Lab 03 • Check the status of the MySQL
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CSC 311: Introduction to Machine Learning Lecture 4 – Multiclass Classification & Neural Networks Based on slides by Amir-massoud Farahmand & Emad A.M. Andrews Intro ML (UofT) CSC311-Lec4 1 / 48 Classification: predicting a discrete-valued target Binary classification: predicting a binary-valued target Multiclass classification: predicting a discrete(> 2)-valued target Examples of multi-class classification
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CIND110 – Lab 04 CIND110 – Data Organization for Data Analysts Lab 04 – More SQL and Schema Modification 1 Running the MySQL Service 2 2 The Working Database Logical Model 3 3 Comparisons Involving Three-valued Logic 4 4 Nested Queries 5 Applying Boolean Functions 6 Renaming Columns in the Results 7 Joining Tables 8
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